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Fabric Data Agents: Unlocking the Power of Agents as a Steppingstone for a Modern Data Platform
What Are Fabric Data Agents? Fabric Data Agents are intelligent, AI-powered assistants embedded within Microsoft Fabric, a unified data platform that integrates data ingestion, processing, transformation, and analytics. These agents act as intermediaries between users and data, enabling seamless interaction through natural language queries in the form of Q&A applications. Whether it's retrieving insights, analyzing trends, or generating visualizations, Fabric Data Agents simplify complex data tasks, making advanced analytics accessible to everyone—from data scientists to business analysts to executive teams. How Do They Work? At the center of Fabric Data Agents is OneLake, a unified and governed data lake that joins data from various sources, including on-premises systems, cloud platforms, and third-party databases. OneLake ensures that all data is stored in a common, open format, simplifying data management and enabling agents to access a comprehensive view of the organization's data. Through Fabric’s Data Ingestion capabilities, such as Fabric Data Factory, OneLake Shortcuts, and Fabric Database Mirroring, Fabric Data Agents are designed to connect with over 200 data sources, ensuring seamless integration across an organization's data estate. This connectivity allows them to pull data from diverse systems and provide a unified analytics experience. Here's how Fabric Data Agents work: Natural Language Processing: Using advanced NLP techniques, Fabric Data Agents enable users to interact with data through conversational queries. For example, users can ask questions like, "What are the top-performing investment portfolios this quarter?" and receive precise answers, grounded on enterprise data. AI-powered Insights: The agents process queries, reason over data, and deliver actionable insights, using Azure OpenAI models, all while maintaining data security and compliance. Customization: Fabric data agents are highly customizable. Users can provide custom instructions and examples to tailor their behavior to specific scenarios. Fabric Data Agents allow users to provide example SQL queries, which can be used to influence the agent’s behavior. They also can integrate with Azure AI Agent Service or Microsoft Copilot Studio, where organizations can tailor agents to specific use cases, such as risk assessment or fraud detection. Security and Compliance: Fabric Data Agents are built with enterprise-grade security features, including inheriting Identity Passthrough/On-Behalf-Of (OBO) authentication. This ensures that business users only access data they are authorized to view, keeping strict compliance with regulations like GDPR and CCPA across geographies and user roles. Integration with Azure: Fabric Data Agents are deeply integrated with Azure services, such as Azure AI Agent Service and Azure OpenAI Service. Practically, organizations can publish Fabric Data Agents to custom Copilots using these services and use the APIs in various custom AI applications. This integration ensures scalability, high availability, and performance and exceptional customer experience. Why Should Financial Services Companies Use Fabric Data Agents? The financial services industry faces unique challenges, including stringent regulatory requirements, the need for real-time decision-making, and empowering users to interact with an AI application in a Q&A fashion over enterprise data. Fabric Data Agents address these challenges head-on through: Enhanced Efficiency: Automate repetitive tasks, freeing up valuable time for employees to focus on strategic initiatives. Improved Compliance: Use robust data governance features to ensure compliance with regulations like GDPR and CCPA. Data-Driven Decisions: Gain deeper insights into customer behavior, market trends, and operational performance. Scalability: Seamlessly scale analytics capabilities to meet the demands of a growing organization, without really investing in building custom AI applications which require deep expertise. Integration with Azure: Fabric Data Agents are natively designed to integrate across Microsoft’s ecosystem, providing a comprehensive end-to-end solution for a Modern Data Platform. How different are Fabric Data Agents from Copilot Studio Agents? Fabric Data Agents and Copilot Studio Agents serve distinct purposes within Microsoft's ecosystem: Fabric Data Agents are tailored for data science workflows. They integrate AI capabilities to interact with organizational data, providing analytics insights. They focus on data processing and analysis using the medallion architecture (bronze, silver, and gold layers) and support integration with the Lakehouse, Data Warehouse, KQL Databases and Semantic Models. Copilot Studio Agents, on the other hand, are customizable AI-powered assistants designed for specific tasks. Built within Copilot Studio, they can connect to various enterprise data sources like OneLake, AI Search, SharePoint, OneDrive, and Dynamics 365. These agents are versatile, enabling businesses to automate workflows, analyze data, and provide contextual responses by using APIs and built-in connectors. What are the technical requirements for using Fabric Data Agents? A paid F64 or higher Fabric capacity resource Fabric data agent tenant settingsis enabled. Copilot tenant switchis enabled. Cross-geo processing for AIis enabled. Cross-geo storing for AIis enabled. At least one of these: Fabric Data Warehouse, Fabric Lakehouse, one or more Power BI semantic models, or a KQL database with data. Power BI semantic models via XMLA endpoints tenant switchis enabled for Power BI semantic model data sources. Final Thoughts In a data-driven world, Fabric Data Agents are poised to redefine how financial services organizations operate and innovate. By simplifying complex data processes, enabling actionable insights, and fostering collaboration across teams, these intelligent agents empower organizations to unlock the true potential of their data. Paired with the robust capabilities of Microsoft Fabric and Azure, financial institutions can confidently navigate industry challenges, drive growth, and deliver superior customer experiences. Adopting Fabric Data Agents is not just an upgrade—it's a transformative step towards building a resilient and future-ready business. The time to embrace the data revolution is now. Learn how to create Fabric Data Agents
Julian_Tanushi
Apr 25, 2025 Place Analytics on Azure Blog
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Llama 4 is now available in Azure Databricks
We are excited to announce the availability of Meta's Llama 4 in Azure Databricks. As you know, enterprises all over the world already use Llama models in Azure Databricks to power AI enterprise agents, workflows, and applications. Now with Llama 4 and Azure Databricks, you can get higher quality, faster inference, and lower cost than previous models. Llama 4 Maverick, the highest-quality and largest Llama model from today's announcement, is built for developers building the next generation of AI products that combine multilingual fluency, image understanding precision, and security. With Maverick on Azure Databricks, you can: Build domain specific AI agents with your data Run scalable inference with your data pipeline Fine-tune for accuracy and Govern AI usage with Mosaic AI Gateway Azure Databricks Intelligence Platform makes it easy for you to securely connect Llama 4 to your enterprise data using Unity Catalog governed tools to build agents with contextual awareness. Enterprise data needs enterprise scale, whether it is to summarize documents or analyze support tickets, but without the infrastructure overhead. With Azure Databricks workflows and Llama 4 at scale, you can use SQL/Python to run LLMs at scale without overhead. You can tune Llama 4 to your custom use case for accuracy and alignment such as assistant behavior or summarization. All this comes with built in security controls and compliant model usage via Azure Databricks Mosaic AI Gateway with PII detection, logging, and policy guardrails on Azure Databricks. Llama 4 is available now in Azure Databricks. More models will become available in phases. Llama 4 Scout is coming soon and you'll be able to pick the model that fits your workload best. Learn more about Llama 4 and supported models in Azure Databricks here and get started today.
AnaviNahar
Apr 07, 2025 Place Analytics on Azure Blog
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Power BI & Azure Databricks: Smarter Refreshes, Less Hassle
We are excited to extend the deep integration between Azure Databricks and Microsoft Power BI with the Public Preview of the Power BI task type in Azure Databricks Workflows. This new capability allows users to update and refresh Power BI semantic models directly from their Azure Databricks workflows, ensuring real-time data updates for reports and dashboards. By leveraging orchestration and triggers within Azure Databricks Workflows, organizations can improve efficiency, reduce refresh costs, and enhance data accuracy for Power BI users. Power BI tasks seamlessly integrate with Unity Catalog in Azure Databricks, enabling automated updates to tables, views, materialized views, and streaming tables across multiple schemas and catalogs. With support for Import, DirectQuery, and Dual Storage modes, Power BI tasks provide flexibility in managing performance and security. This direct integration eliminates manual processes, ensuring Power BI models stay synchronized with underlying data without requiring context switching between platforms. Built into Azure Databricks Lakeflow, Power BI tasks benefit from enterprise-grade orchestration and monitoring, including task dependencies, scheduling, retries, and notifications. This streamlines workflows and improves governance by utilizing Microsoft Entra ID authentication and Unity Catalog suite of security and governance offerings. We invite you to explore the new Power BI tasks today and experience seamless data integration—get started by visiting the [ADB Power BI task documentation].
LindseyAllen
Apr 01, 2025 Place Analytics on Azure Blog
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Delivering Information with Azure Synapse and Data Vault 2.0
Data Vault has been designed to integrate data from multiple data sources, creatively destruct the data into its fundamental components, and store and organize it so that any target structure can be derived quickly. This article focused on generating information models, often dimensional models, using virtual entities. They are used in the data architecture to deliver information. After all, dimensional models are easier to consume by dashboarding solutions, and business users know how to use dimensions and facts to aggregate their measures. However, PIT and bridge tables are usually needed to maintain the desired performance level. They also simplify the implementation of dimension and fact entities and, for those reasons, are frequently found in Data Vault-based data platforms. This article completes the information delivery. The following articles will focus on the automation aspects of Data Vault modeling and implementation.
Naveed-Hussain
Mar 28, 2025 Place Analytics on Azure Blog
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Creating a AI-Driven Chatbot to Inquire Insights into business data
Introduction In the fast-paced digital era, the ability to extract meaningful insights from vast datasets is paramount for businesses striving for a competitive edge. Microsoft Dynamics 365 Finance and Operations (D365 F&O) is a robust ERP platform, generating substantial business data. To unlock the full potential of this data, integrating it with advanced analytics and AI tools such as Azure OpenAI, Azure Synapse Workspace, or Fabric Workspace is essential. This blog will guide you through the process of creating a chatbot to inquire insights using Azure OpenAI with Azure Synapse Workspace or Fabric Workspace. Architecture Natural Language Processing (NLP): Enables customers to inquire about business data such as order statuses, item details, and personalized order information using natural language. Seamless Data Integration: Real-time data fetching from D365 F&O for accurate and up-to-date information. Contextual and Personalized Responses: AI provides detailed, context-rich responses to customer queries, improving engagement and satisfaction. Scalability and Efficiency: Handles multiple concurrent inquiries, reducing the burden on customer service teams and improving operational efficiency. Understanding the Components Microsoft Dynamics 365 Finance and Operations (D365 F&O) D365 F&O is a comprehensive ERP solution designed to help businesses streamline their operations, manage finances, and control supply chain activities. It generates and stores vast amounts of transactional data essential for deriving actionable insights. Dataverse Dataverse is a cloud-based data storage solution that allows you to securely store and manage data used by business applications. It provides a scalable and reliable platform for data integration and analytics, enabling businesses to derive actionable insights from their data. Azure Synapse Analytics Azure Synapse Analytics is an integrated analytics service that brings together big data and data warehousing. It allows users to query data on their terms, deploying either serverless or provisioned resources at scale. The service provides a unified experience to ingest, prepare, manage, and serve data for instant business intelligence and machine learning requirements. Fabric Workspace Fabric Workspace provides a collaborative platform for data scientists, analysts, and business users to work together on data projects. It facilitates the seamless integration of various data sources and advanced analytics tools to drive innovative solutions. Azure SQL Database Azure SQL Database is a cloud-based relational database service built on Microsoft SQL Server technologies. It offers a range of deployment options, including single databases, elastic pools, and managed instances, allowing you to choose the best fit for your application needs. Azure SQL Database provides high availability, scalability, and security features, making it an ideal choice for modern applications. Data from Dynamics 365 Finance and Operations (F&O) is copied to an Azure SQL Database using a flow that involves Azure Data Lake Storage (ADLS) and Azure Data Factory (ADF) Azure OpenAI Azure OpenAI enables developers to build and deploy intelligent applications using powerful AI models. By integrating OpenAI’s capabilities with Azure’s infrastructure, businesses can create sophisticated solutions that leverage natural language processing, machine learning, and advanced analytics. Step-by-Step Guide to Creating the Chatbot Step 1: Export Data from D365 F&O To begin, export the necessary data from your D365 F&O instance. This data will serve as the foundation for your analytics and AI operations. Ensure the exported data is in a format compatible with Azure Synapse or Fabric Workspace. Step 2: Ingest Data into Azure Synapse Workspace or Fabric Workspace Next, ingest the exported data into Azure Synapse Workspace or Fabric Workspace. Utilize the workspace’s capabilities to prepare, manage, and optimize the data for further analysis. This step involves setting up data pipelines, cleaning the data, and transforming it into a suitable format for processing. Step 3: Set Up Azure OpenAI With your data ready, set up Azure OpenAI in your environment. This involves provisioning the necessary resources, configuring the OpenAI service, and integrating it with your Azure infrastructure. Ensure you have the appropriate permissions and access controls in place. Step 4: Develop the Chatbot Develop the chatbot using Azure OpenAI’s capabilities. Design the chatbot to interact with users naturally, allowing them to inquire insights and receive valuable information based on the data from D365 F&O. Utilize natural language processing to enhance the chatbot’s ability to understand and respond to user queries effectively. Step 5: Integrate the Chatbot with Azure Synapse or Fabric Workspace Integrate the developed chatbot with Azure Synapse Workspace or Fabric Workspace. This integration will enable the chatbot to access and analyze the ingested data, providing users with real-time insights. Set up the necessary APIs and data connections to facilitate seamless communication between the chatbot and the workspace. Step 6: Test and Refine the Chatbot Thoroughly test the chatbot to ensure it functions as expected. Address any issues or bugs, and refine the chatbot’s responses and capabilities. This step is crucial to ensure the chatbot delivers accurate and valuable insights to users. Best Practices for Data Access Data Security Data security is paramount when exporting sensitive business information. Implement the following best practices: Ensure that all data transfers are encrypted using secure protocols. Use role-based access control to restrict access to the data exported. Regularly audit and monitor data export activities to detect any unauthorized access or anomalies. Data Transformation Transforming data before accessing it can enhance its usability for analysis: Use Synapse data flows to clean and normalize the data. Apply business logic to enrich the data with additional context. Aggregate and summarize data to improve query performance. Monitoring and Maintenance Regular monitoring and maintenance ensure the smooth operation of your data export solution: Set up alerts and notifications for any failures or performance issues in the data pipelines. Regularly review and optimize the data export and transformation processes. Keep your Azure Synapse environment up to date with the latest features and enhancements. Benefits of Integrating AI and Advanced Analytics Enhanced Decision-Making By leveraging AI and advanced analytics, businesses can make data-driven decisions. The chatbot provides timely insights, enabling stakeholders to act quickly and efficiently. Improved Customer Experience A chatbot enhances customer interactions by providing instant responses and personalized information. This leads to higher satisfaction and engagement levels. Operational Efficiency Integrating AI tools with business data streamlines operations, reduces manual efforts, and increases overall efficiency. Businesses can optimize processes and resource allocation effectively. Scalability It can handle multiple concurrent inquiries, scaling as the business grows without requiring proportional increases in customer service resources. Conclusion Creating a chatbot to inquire insights using Azure OpenAI with Azure Synapse Workspace or Fabric Workspace represents a significant advancement in how businesses can leverage their data. By following the steps outlined in this guide, organizations can develop sophisticated AI-driven solutions that enhance decision-making, improve customer experiences, and drive operational efficiency. Embrace the power of AI and advanced analytics to transform your business and unlock new opportunities for growth.
Khushhalgarg
Mar 27, 2025 Place Analytics on Azure Blog
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Anthropic State-of-the-Art Models Available to Azure Databricks Customers
Our customers now have greater model choices with the arrival of Anthropic Claude 3.7 Sonnet in Azure Databricks. Databricks is announcing a partnership with Anthropic to integrate their state-of-the-art models into Databricks Data Intelligence Platform as a native offering, starting with Claude 3.7 Sonnet http://databricks.com/blog/anthropic-claude-37-sonnet-now-natively-available-databricks. With this announcement, Azure customers can use Claude Models directly in Azure Databricks. Foundation model REST API reference - Azure Databricks | Microsoft Learn With Anthropic models available in Azure Databricks, customers can use the Claude "think" tool with business data optimized promote to guide Claude efficiently perform complex tasks. With Claude models in Azure Databricks, enterprises can deliver domain-specific, high quality AI agents more efficiently. As an integrated component of the Azure Databricks Data Intelligence Platform, Anthropic Claude models benefit from comprehensive end-to-end governance and monitoring throughout the entire data and AI lifecycle with Unity Catalog. With Claude models, we remain committed to providing customers with model flexibility. Through the Azure Databricks Data Intelligence Platform, customers can securely connect to any model provider and select the most suitable model for their needs. They can further enhance these models with enterprise data to develop domain-specific, high-quality AI agents, supported by built-in custom evaluation governance across both data and models.
LindseyAllen
Mar 27, 2025 Place Analytics on Azure Blog
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Data Vault 2.0 Warehouse Automation on Azure
This is the series of 'Blog Articles' on the topic "Data Vault 2.0 on Azure" where we start from 'What?' and then slowly dwell into 'How To?' implement DV 2.0 on Azure Data Platform Technologies.
Naveed-Hussain
Mar 07, 2025 Place Analytics on Azure Blog
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Workarounds for Maven Json-smart 2.5.2 Release Breaking Azure Databricks Job Dependencies
A recent Maven library release corrupted json-smart’s central metadata, ended up removing all previous versions of Maven library other than 2.5.2. This results in DBR ivy resolution failures if customers’ job has transitive dependency on previous version of json-smart. The impact to Azure Databricks customers is jobs failure and in some cases the job clusters could fail to start. More details and updates of the issue can be found here: https://github.com/netplex/json-smart-v2/issues/240. This also affects google’s maven mirror, which is used by DBR 11+ to resolve maven libraries, and maven central is used as the backup for google’s maven mirror. To mitigate the issue, you can take the workaround steps to install the customer’s requested library and smart-json separately Steps 1: Install the customer’s requested library (use azure-eventhubs-spark as an example) but exclude json-smart. Step 2: Install net.minidev:json-smart:2.3 (or any other version that was needed) Workaround for run time, if you have a preferred Maven mirror that is not affected by this issue, or if you host a private Maven mirror, you can configure your Databricks environment to use it. Step: Set the Spark configuration parameter spark.databricks.driver.preferredMavenCentralMirrorUrl to your preferred mirror repository URL.
LindseyAllen
Feb 12, 2025 Place Analytics on Azure Blog
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Securing Azure HDInsight: ESM Support with Ubuntu 18.04, Cluster Updates, and Best Practices
Azure HDInsight, Microsoft's cloud-based big data analytics platform, continues to advance its features to provide users with a secure and efficient environment. In this article, we will explore the latest enhancements, focusing on Expanded Security Maintenance (ESM) support, the importance of regular cluster updates, and best practices recommended by Microsoft to fortify HDInsight deployments. The foundation of a secure Azure HDInsight environment lies in its ability to address critical vulnerabilities promptly. Microsoft ensures this by shipping the latest HDInsight images with Expanded Security Maintenance (ESM) support, which provides a framework for ongoing support, stability with minimal changes specifically targeting critical, high and some medium-level fixes. This ensures that HDInsight users benefit from a continuously updated and secure environment. ESM Support in Latest Images: Azure HDInsight 5.0 and 5.1 versions use Ubuntu 18.04 pro image. Ubuntu Pro includes security patching for all Ubuntu packages due to Expanded Security Maintenance (ESM) for Infrastructure and Applications. Ubuntu Pro 18.04 LTS will remain fully supported until April 2028. For more information on what's new in the latest HDInsight images with ESM support, users can refer to the official release notes on the Azure HDInsight Release Notes Archive. Periodic Cluster Updates: Maintaining a secure HDInsight environment requires diligence in keeping clusters up to date. Microsoft facilitates this process through the HDInsight OS patching mechanism. Periodically updating clusters using the procedures outlined in the official documentation ensures that users benefit from the latest features, performance improvements, and crucial security patches. Learn more about updating HDInsight clusters through the Azure HDInsight OS Patching documentation. ESM and HDI Release Integration: Extended Security Maintenance is seamlessly integrated into HDInsight releases. As part of each HDInsight release, critical fixes provided by ESM are bundled. This ensures that users benefit from the latest security enhancements with each new release. Customer Recommendation: Use the Latest Image: To maximize the benefits of the latest features and security updates, customers are strongly recommended to use the most recent HDInsight image number. By doing so, organizations ensure that their HDInsight clusters are fortified against the latest threats and vulnerabilities. Accessing Fixed CVE Details: For users seeking detailed information about the fixed Common Vulnerabilities and Exposures (CVEs), the Ubuntu CVE site serves as a valuable resource. Here, users can access comprehensive insights into the specific vulnerabilities addressed in each release, empowering them to make informed decisions about their security posture.
ApurbaSR
Jan 15, 2025 Place Analytics on Azure Blog
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Efficient Log Management with Microsoft Fabric
Introduction In the era of digital transformation, managing and analyzing log files in real-time is essential for maintaining application health, security, and performance. There are many 3rd party solutions in this area allowing collecting / processing storing, analyzing and acting upon this data source. But sometimes as your systems scale, those solution can become very costly, their cost model increases based on the amount of ingested data and not according to the real resources utilization or customer value This blog post explores a robust architecture leveraging Microsoft Fabric SaaS platform focused on its Realtime Intelligence capabilities for efficient log files collection processing and analysis. The use cases can vary from simple application errors troubleshooting, to more advanced use case such as application trends detection: detecting slowly degrading performance issues: like average user session in the app for specific activities last more than expected to more proactive monitoring using log based KPIs definition and monitoring those APIS for alerts generation Regarding cost , since Fabric provides a complete separation between compute and storage you can grow your data without necessarily growing your compute costs and you still pay for the resources that re used in a pay as you go model. Architecture Overview The proposed architecture integrates Microsoft Fabric’s Real time intelligence (Realtime Hub) with your source log files to create a seamless, near real-time log collection solution It is based on Microsoft Fabric: a SAAS solution which is a unified suite integrating several best of breed Microsoft analytical experiences. Fabric is a modern data/ai platform based on unified and open data formats (parquet/delta) allowing both classical data lakes experiences using both traditional Lakehouse/warehouse SQL analytics as well as real-time intelligence on semi structured data , all in on a lake-centric SaaS platform. Fabric's open foundation with built-in governance enables you to connect to various clouds and tools while maintaining data trust. This is High level Overview of Realtime Intelligence inside Fabric Log events - Fabric based Architecture When looking in more details a solution for log collection processing storage and analysis we propose the following architecture Now let's discuss it in more details: General notes: Since Fabric is a SAAS solution, all the components can be used without deploying any infrastructure in advance, just by a click of a button and very simple configurations you can customize the relevant components for this solution The main components used in this solution are Data Pipeline Onelake and Eventhouse Our data source for this example is taken from this public git repo: https://github.com/logpai/loghub/tree/master/Spark The files were taken and stored inside an S3 bucket to simulate the easiness of the data pipeline integration to external data sources. A typical log file looks like this : 16/07/26 12:00:30 INFO util.Utils: Successfully started service 'sparkDriverActorSystem' on port 59219. 16/07/26 12:00:30 INFO spark.SparkEnv: Registering MapOutputTracker 16/07/26 12:00:30 INFO spark.SparkEnv: Registering BlockManagerMaster 16/07/26 12:00:30 INFO storage.DiskBlockManager: Created local directory at /opt/hdfs/nodemanager/usercache/curi/appcache/application_1460011102909_0176/blockmgr-5ea750cb-dd00-4593-8b55-4fec98723714 16/07/26 12:00:30 INFO storage.MemoryStore: MemoryStore started with capacity 2.4 GB Components Data Pipeline First challenge to solve is how to bring the log files from your system into Fabric this is the Log collection phase: many solutions exist for this phase each with its pros and cons In Fabric the standard approach to bring data in is by use of Copy Activity in ADF or in its Fabric SAAS version is now called Data Pipeline: Data pipeline is a low code / no code tool allowing to manage and automate the process of moving and transforming data within Microsoft Fabric, a serverless ETL tool with more than 100 connectors enabling integration with a wide variety of data sources, including databases, cloud services, file systems, and more. In addition, it supports an on prem agent called self-hosted integration runtime, this agent that you install on a VM, acts as a bridge allowing to run your pipeline on a local VM and securing your connection from on prem network to the cloud Let’s describe in more details our solution data pipeline: Bear in mind ADF is very flexible and supports reading at scale from a wide range of data sources / files integrated as well to all major cloud vendors from blob storage retrieval : like S3, GCS, Oracle Cloud, File systems, FTP/SFTP etc so that even if your files are generated externally to Azure this is not an issue at all. Visualization of Fabric Data Pipeline Log Collection ADF Copy Activity: Inside Data pipeline we will create an Activity called Copy Activity with the following basic config Source: mapped to your data sources: it can be azure blob storage with container containing the log files, other cloud object storage like S3 or GCS , log files will be retrieved in general from a specific container/folder and are fetched based on some prefix/suffix in the file name. To support incremental load process we can configure it to delete the source files that it reads so that once the files are successfully transferred to their target they will be automatically deleted from their source . On the next iteration pipeline will not have to process the same files again. Sink: Onelake/Lakehouse folder: we create ahead of time a Lakehouse which is an abstract data container allowing to hold and manage at scale your data either structured or unstructured, we will then select it from the list of connectors (look for Onelake/Lakehouse) Log Shippers: This is an optional component, sometimes it is not allowed for the ETL to access your OnPrem Vnet , in this case tools like Fluentd , Filebeat , Open Telemetry collector used to forward your application collected logs to the main entry point of the system: the Azure Blob Storage. Azcopy CLI: if you don’t wish to invest into expensive tools and all you need to copy your data in a scale/secure manner to Azure Storage, you might consider create your own log shipping solution based on the free Azcopy tool together withs some basic scripting around it for scheduling: Azcopy is a command-line utility designed for high-performance uploading, downloading, and copying data to and from Microsoft Azure Blob and File storage. Fabric first Activity : Copy from Source Bucket to Lakehouse Log Preparation Upon log files landing in the azure blob storage, EventStream can be used to trigger the Data Pipeline that will handle the data preparation and loading phase. So what is Data preparation phase’s main purpose? After the log files land in the storage and before they are loaded to the realtime logs database the KQL Database , it might be necessary to transform the data with some basic manipulations . The reasons for that might be different A Few examples Bad data formats: for example, sometimes logs files contain problematic characters like new lines inside a row (stack trace error message with new lines as part of the message field of the record) Metadata enrichment: sometimes the log file names contain in their name some meaningful data : for example file name describes the originating process name / server name , so this metadata can be lost once the file content is loaded into database Regulation restrictions: sometimes logs contain private data like names, credit card numbers, social security number etc called PII that must be removed , hashed or encrypted before the load to database In our case we will be running a pyspark notebook who reads the files from Onelake folder, fixes the new lines inside a row issue, and create new files in another Onelake folder, we call this notebook with a base parameter called log_path that defines the log files location on the Onelake to read from Fabric second Activity : Running the Notebook Log Loading Inside Data pipeline , the last step, after the transformation phase, we call again the Copy data activity but this time source and sink are differen: Source: Lakehouse folder (previous notebook output) Sink: Evenhouse specific Table (created ahead of time): it is basically an empty table (lograw) Visualization of Fabric last Activity : Loading to EventHouse In summary for this stage the log collection and preparation: we broke this into 3 data pipeline activities: Copy Activity: Read the log files from source: This is the first step of the log ingestion pipeline it is running inside our orchestrator Data pipeline. Run Notebook Activity : Transform the log files : this is the execution of a single or chain of notebooks Copy Activity : Load the log files into destination datatbase : KQL inside Evenhouse : the logs database table called lograw, it is a specific table created ahead of time inside EventHouse Database Inside The Eventhouse We needed to create a KQL database with a table to hold the raw ingested log records KQL datbase is a scalable and efficient storage solution for log files, optimized for high-volume data ingestion and retrieval. Eventhouses and KQL databases operate on a fully managed Kusto engine. With an Eventhouse or KQL database, you can expect available compute for your analytics within 5 to 10 seconds. The compute resources grow with your data analytic needs. Log Ingestion to KQL Database with Update Policy We can separate the ETL transformation logic of what happens to the data before, it reaches the Eventhouse KQL database and after that. Before it reached the database , the only transformation we did was calling during the data pipeline a notebook to handle the new lines merge logic, This cannot be easily done as part of the database ingestion logic simply because when we try to load the files with new lines as part of a field of a record , it breaks the convention and what happens is that the ingestion process creates separate table records for each new line of the exceptions stacktrace. On the other hand, we might need to define basic transformation rules: such as date formatting, type conversion (string to numbers) , parse and extract some interesting value from a String based on regular exception, create JSON (dynamic type) of a hierarchical string (XML / JSON string etc) for all these transformations we can work with what is called an update policy we can define a simple ETL logic inside KQL database as explained here During this step we create from logsraw staging table a new table called logparsed , that will be our destination final table for the log queries. Those are the KQL Tables defined to hold the log files .create table logsraw ( timestamp:string , log_level:string, module:string, message:string) .create table logsparsed ( formattedDatetime:datetime , log_level:string, log_module:string, message:string) This is the update policy that automatically converts data from, the staging table logsraw to the destination table logparsed .create-or-alter function parse_lograw() { logsraw | project formattedtime = todatetime(strcat("20", substring(timestamp, 0, 2), "-", substring(timestamp, 3, 2), "-", substring(timestamp, 6, 2), "T", substring(timestamp, 9, 8))), log_level, logmodule=module, message } .alter table logsparsed policy update @'[{ "IsEnabled": true, "Source": "logsraw", "Query": "parse_lograw()", "IsTransactional": true, "PropagateIngestionProperties": true}]' Since we don't need to retain the data in the staging table (lograw) we can define a retention policy of 0 TTL like this : .alter-merge table logsraw policy retention softdelete = 0sec recoverability = disabled Query Log files After data is ingested and transformed it lands in a basic logs table that is schematized : logparsed, in general we have some common fields that are mapped to their own columns like : log level (INFO/ ERROR/ DEBUG) , log category , log timestamp (a datetime typed column) and log message which can be in general either a simple error string or a complex JSON formatted string in which case it is usually preferred to be converted to dynamic type that will bring additional benefits like simplified query logic, and reduced data processing (to avoid expensive joins) Example for Typical Log Queries Category Purpose KQL Query Troubleshooting Looking for an error at specific datetime range logsparsed | where message contains "Exception" and formattedDatetime between ( datetime(2016-07-26T12:10:00) .. datetime(2016-07-26T12:20:00)) Statistics Basic statistics Min/Max timestamp of log events logsparsed | summarize minTimestamp=min(formattedDatetime), maxTimestamp=max(formattedDatetime) Exceptions Stats Check Exceptions Distributions logsparsed | extend exceptionType = case(message contains "java.io.IOException","IOException", message contains "java.lang.IllegalStateException","IllegalStateException", message contains "org.apache.spark.rpc.RpcTimeoutException", "RpcTimeoutException", message contains "org.apache.spark.SparkException","SparkException", message contains "Exception","Other Exceptions", "No Exception") | where exceptionType != "No Exception" | summarize count() by exceptionType Log Module Stats Check Modules Distribution logsparsed | summarize count() by log_module | order by count_ desc | take 10 Realtime Dashboards After querying the logs, it is possible to visualize the query results in Realtime dashboards, for that all what’s required Select the query Click on Pin to Dashboard After adding the queries to tiles inside the dashboard this is a typical dashboard we can easily build: Realtime dashboards can be configured to be refreshed in Realtime like illustrated here: in which case user can very easily configure how often to refresh the queries and visualization : at the extreme case it can be as low as Continuus There are many more capabilities implemented in the Real-Time Dashboard, like data exploration Alerting using Data Activator , conditional formatting (change items colors based on KPIs threshold) and this framework and capabilities are heavily invested and keep growing. What about AI Integration ? Machine Learning Models: Kusto supports out of the box time series analysis allowing for example anomaly detection: https://learn.microsoft.com/en-us/fabric/real-time-intelligence/dashboard-explore-data and clustering but if it’s not enough for you, you can always mirror the data of your KQL tables into Onelake delta parquet format by selecting OneLake availability This configuration will create another copy of your data in open format delta parquet : you have it available for any Spark/Python/SparkML/SQL analytics for whatever machine learning exploration and ML modeling you wish to explore train and serve This is illustrated here : Bear in mind , there is no additional storage cost to turn on OneLake availability Conclusion A well-designed real-time intelligence solution for log file management using Microsoft Fabric and EventHouse can significantly enhance an organization’s ability to monitor, analyze, and respond to log events. By leveraging modern technologies and best practices, organizations can gain valuable insights and maintain robust system performance and security.
rabindori
Jan 10, 2025 Place Analytics on Azure Blog
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Yet, managing and using vast amounts of data across complex systems can be a daunting challenge. Introducing Fabric Data Agents—a groundbreaking solution designed to revolutionize how financial institutions harness the power of their enterprise data.","metrics":{"__typename":"MessageMetrics","views":476},"postTime":"2025-04-24T21:51:24.559-07:00","lastPublishTime":"2025-04-24T21:51:24.559-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" What Are Fabric Data Agents? \n Fabric Data Agents are intelligent, AI-powered assistants embedded within Microsoft Fabric, a unified data platform that integrates data ingestion, processing, transformation, and analytics. These agents act as intermediaries between users and data, enabling seamless interaction through natural language queries in the form of Q&A applications. Whether it's retrieving insights, analyzing trends, or generating visualizations, Fabric Data Agents simplify complex data tasks, making advanced analytics accessible to everyone—from data scientists to business analysts to executive teams. \n How Do They Work? \n At the center of Fabric Data Agents is OneLake, a unified and governed data lake that joins data from various sources, including on-premises systems, cloud platforms, and third-party databases. OneLake ensures that all data is stored in a common, open format, simplifying data management and enabling agents to access a comprehensive view of the organization's data. \n Through Fabric’s Data Ingestion capabilities, such as Fabric Data Factory, OneLake Shortcuts, and Fabric Database Mirroring, Fabric Data Agents are designed to connect with over 200 data sources, ensuring seamless integration across an organization's data estate. This connectivity allows them to pull data from diverse systems and provide a unified analytics experience. \n Here's how Fabric Data Agents work: \n \n Natural Language Processing: Using advanced NLP techniques, Fabric Data Agents enable users to interact with data through conversational queries. For example, users can ask questions like, \"What are the top-performing investment portfolios this quarter?\" and receive precise answers, grounded on enterprise data. \n AI-powered Insights: The agents process queries, reason over data, and deliver actionable insights, using Azure OpenAI models, all while maintaining data security and compliance. \n Customization: Fabric data agents are highly customizable. Users can provide custom instructions and examples to tailor their behavior to specific scenarios. Fabric Data Agents allow users to provide example SQL queries, which can be used to influence the agent’s behavior. They also can integrate with Azure AI Agent Service or Microsoft Copilot Studio, where organizations can tailor agents to specific use cases, such as risk assessment or fraud detection. \n Security and Compliance: Fabric Data Agents are built with enterprise-grade security features, including inheriting Identity Passthrough/On-Behalf-Of (OBO) authentication. This ensures that business users only access data they are authorized to view, keeping strict compliance with regulations like GDPR and CCPA across geographies and user roles. \n Integration with Azure: Fabric Data Agents are deeply integrated with Azure services, such as Azure AI Agent Service and Azure OpenAI Service. Practically, organizations can publish Fabric Data Agents to custom Copilots using these services and use the APIs in various custom AI applications. This integration ensures scalability, high availability, and performance and exceptional customer experience. \n \n Why Should Financial Services Companies Use Fabric Data Agents? \n The financial services industry faces unique challenges, including stringent regulatory requirements, the need for real-time decision-making, and empowering users to interact with an AI application in a Q&A fashion over enterprise data. Fabric Data Agents address these challenges head-on through: \n \n Enhanced Efficiency: Automate repetitive tasks, freeing up valuable time for employees to focus on strategic initiatives. \n Improved Compliance: Use robust data governance features to ensure compliance with regulations like GDPR and CCPA. \n Data-Driven Decisions: Gain deeper insights into customer behavior, market trends, and operational performance. \n Scalability: Seamlessly scale analytics capabilities to meet the demands of a growing organization, without really investing in building custom AI applications which require deep expertise. \n Integration with Azure: Fabric Data Agents are natively designed to integrate across Microsoft’s ecosystem, providing a comprehensive end-to-end solution for a Modern Data Platform. \n \n How different are Fabric Data Agents from Copilot Studio Agents? \n Fabric Data Agents and Copilot Studio Agents serve distinct purposes within Microsoft's ecosystem: \n \n Fabric Data Agents are tailored for data science workflows. They integrate AI capabilities to interact with organizational data, providing analytics insights. They focus on data processing and analysis using the medallion architecture (bronze, silver, and gold layers) and support integration with the Lakehouse, Data Warehouse, KQL Databases and Semantic Models. \n Copilot Studio Agents, on the other hand, are customizable AI-powered assistants designed for specific tasks. Built within Copilot Studio, they can connect to various enterprise data sources like OneLake, AI Search, SharePoint, OneDrive, and Dynamics 365. These agents are versatile, enabling businesses to automate workflows, analyze data, and provide contextual responses by using APIs and built-in connectors. \n \n What are the technical requirements for using Fabric Data Agents? \n \n A paid F64 or higher Fabric capacity resource \n Fabric data agent tenant settingsis enabled. \n Copilot tenant switchis enabled. \n Cross-geo processing for AIis enabled. \n Cross-geo storing for AIis enabled. \n At least one of these: Fabric Data Warehouse, Fabric Lakehouse, one or more Power BI semantic models, or a KQL database with data. \n Power BI semantic models via XMLA endpoints tenant switchis enabled for Power BI semantic model data sources. \n \n Final Thoughts \n In a data-driven world, Fabric Data Agents are poised to redefine how financial services organizations operate and innovate. By simplifying complex data processes, enabling actionable insights, and fostering collaboration across teams, these intelligent agents empower organizations to unlock the true potential of their data. Paired with the robust capabilities of Microsoft Fabric and Azure, financial institutions can confidently navigate industry challenges, drive growth, and deliver superior customer experiences. \n Adopting Fabric Data Agents is not just an upgrade—it's a transformative step towards building a resilient and future-ready business. The time to embrace the data revolution is now. \n Learn how to create Fabric Data Agents \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"6532","kudosSumWeight":2,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4401277":{"__typename":"Conversation","id":"conversation:4401277","topic":{"__typename":"BlogTopicMessage","uid":4401277},"lastPostingActivityTime":"2025-04-06T22:18:30.356-07:00","solved":false},"User:user:803999":{"__typename":"User","uid":803999,"login":"AnaviNahar","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS04MDM5OTktQlh3V0lu?image-coordinates=0%2C64%2C670%2C735"},"id":"user:803999"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMjc3LWFSODQwUQ?revision=17\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMjc3LWFSODQwUQ?revision=17","title":"image.png","associationType":"BODY","width":358,"height":80,"altText":""},"BlogTopicMessage:message:4401277":{"__typename":"BlogTopicMessage","subject":"Llama 4 is now available in Azure Databricks","conversation":{"__ref":"Conversation:conversation:4401277"},"id":"message:4401277","revisionNum":17,"uid":4401277,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:803999"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"Bring the power of Azure Databricks with open-source Llama 4 to your AI agents\n","metrics":{"__typename":"MessageMetrics","views":1160},"postTime":"2025-04-05T17:31:47.869-07:00","lastPublishTime":"2025-04-06T22:18:30.356-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n We are excited to announce the availability of Meta's Llama 4 in Azure Databricks. \n As you know, enterprises all over the world already use Llama models in Azure Databricks to power AI enterprise agents, workflows, and applications. Now with Llama 4 and Azure Databricks, you can get higher quality, faster inference, and lower cost than previous models. Llama 4 Maverick, the highest-quality and largest Llama model from today's announcement, is built for developers building the next generation of AI products that combine multilingual fluency, image understanding precision, and security. With Maverick on Azure Databricks, you can: \n \n Build domain specific AI agents with your data \n Run scalable inference with your data pipeline \n Fine-tune for accuracy and \n Govern AI usage with Mosaic AI Gateway \n \n Azure Databricks Intelligence Platform makes it easy for you to securely connect Llama 4 to your enterprise data using Unity Catalog governed tools to build agents with contextual awareness. \n Enterprise data needs enterprise scale, whether it is to summarize documents or analyze support tickets, but without the infrastructure overhead. With Azure Databricks workflows and Llama 4 at scale, you can use SQL/Python to run LLMs at scale without overhead. \n You can tune Llama 4 to your custom use case for accuracy and alignment such as assistant behavior or summarization. \n All this comes with built in security controls and compliant model usage via Azure Databricks Mosaic AI Gateway with PII detection, logging, and policy guardrails on Azure Databricks. \n Llama 4 is available now in Azure Databricks. More models will become available in phases. Llama 4 Scout is coming soon and you'll be able to pick the model that fits your workload best. Learn more about Llama 4 and supported models in Azure Databricks here and get started today. \n \n \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1936","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxMjc3LWFSODQwUQ?revision=17\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4398388":{"__typename":"Conversation","id":"conversation:4398388","topic":{"__typename":"BlogTopicMessage","uid":4398388},"lastPostingActivityTime":"2025-04-01T01:52:37.580-07:00","solved":false},"User:user:201631":{"__typename":"User","uid":201631,"login":"LindseyAllen","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yMDE2MzEtNTgyNzc3aTc0MEE1QkQxRDZFMjYyMDc"},"id":"user:201631"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LU9kNGp1RQ?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LU9kNGp1RQ?revision=3","title":"image.png","associationType":"BODY","width":559,"height":125,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LXFIeXpDTg?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LXFIeXpDTg?revision=3","title":"image (6).png","associationType":"BODY","width":1920,"height":1080,"altText":""},"BlogTopicMessage:message:4398388":{"__typename":"BlogTopicMessage","subject":"Power BI & Azure Databricks: Smarter Refreshes, Less Hassle","conversation":{"__ref":"Conversation:conversation:4398388"},"id":"message:4398388","revisionNum":3,"uid":4398388,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:201631"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"We are thrilled to introduce a new Power BI task type to Azure Databricks Workflows!","metrics":{"__typename":"MessageMetrics","views":1412},"postTime":"2025-03-31T05:02:33.313-07:00","lastPublishTime":"2025-03-31T13:59:40.483-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n We are excited to extend the deep integration between Azure Databricks and Microsoft Power BI with the Public Preview of the Power BI task type in Azure Databricks Workflows. This new capability allows users to update and refresh Power BI semantic models directly from their Azure Databricks workflows, ensuring real-time data updates for reports and dashboards. By leveraging orchestration and triggers within Azure Databricks Workflows, organizations can improve efficiency, reduce refresh costs, and enhance data accuracy for Power BI users. \n Power BI tasks seamlessly integrate with Unity Catalog in Azure Databricks, enabling automated updates to tables, views, materialized views, and streaming tables across multiple schemas and catalogs. With support for Import, DirectQuery, and Dual Storage modes, Power BI tasks provide flexibility in managing performance and security. This direct integration eliminates manual processes, ensuring Power BI models stay synchronized with underlying data without requiring context switching between platforms. \n Built into Azure Databricks Lakeflow, Power BI tasks benefit from enterprise-grade orchestration and monitoring, including task dependencies, scheduling, retries, and notifications. This streamlines workflows and improves governance by utilizing Microsoft Entra ID authentication and Unity Catalog suite of security and governance offerings. \n \n We invite you to explore the new Power BI tasks today and experience seamless data integration—get started by visiting the [ADB Power BI task documentation]. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1569","kudosSumWeight":0,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LU9kNGp1RQ?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4Mzg4LXFIeXpDTg?revision=3\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4390578":{"__typename":"Conversation","id":"conversation:4390578","topic":{"__typename":"BlogTopicMessage","uid":4390578},"lastPostingActivityTime":"2025-03-28T10:12:26.801-07:00","solved":false},"User:user:1741331":{"__typename":"User","uid":1741331,"login":"Naveed-Hussain","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-2.svg?time=0"},"id":"user:1741331"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzkwNTc4LXVOQndybw?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzkwNTc4LXVOQndybw?revision=1","title":"url_upload.jpg","associationType":"COVER","width":4195,"height":2802,"altText":""},"BlogTopicMessage:message:4390578":{"__typename":"BlogTopicMessage","subject":"Delivering Information with Azure Synapse and Data Vault 2.0","conversation":{"__ref":"Conversation:conversation:4390578"},"id":"message:4390578","revisionNum":1,"uid":4390578,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:1741331"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Data Vault has been designed to integrate data from multiple data sources, creatively destruct the data into its fundamental components, and store and organize it so that any target structure can be derived quickly. This article focused on generating information models, often dimensional models, using virtual entities. They are used in the data architecture to deliver information. After all, dimensional models are easier to consume by dashboarding solutions, and business users know how to use dimensions and facts to aggregate their measures. However, PIT and bridge tables are usually needed to maintain the desired performance level. They also simplify the implementation of dimension and fact entities and, for those reasons, are frequently found in Data Vault-based data platforms. \n \n This article completes the information delivery. The following articles will focus on the automation aspects of Data Vault modeling and implementation. ","introduction":"In this series' previous blog articles, we created a Raw Data Vault to store our raw data. In addition to capturing and integrating the data, we applied examples of soft business rules inside the Business Vault. This article focuses on using the data from the combined Data Vault model (that is, both the Raw Data Vault and Business Vault) and transforming it into valuable information to provide to business users.","metrics":{"__typename":"MessageMetrics","views":414},"postTime":"2025-03-07T01:40:34.928-08:00","lastPublishTime":"2025-03-07T01:40:34.928-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n This Article is Authored By Michael Olschimke, co-founder and CEO at Scalefree International GmbH and Co-authored with Tim Kirschke Senior BI Consultant from Scalefree \n The Technical Review is done by Ian Clarke and Naveed Hussain – GBBs (Cloud Scale Analytics) for EMEA at Microsoft \n \n \n Introduction \n In this series' previous blog articles, we created a Raw Data Vault to store our raw data. In addition to capturing and integrating the data, we applied examples of soft business rules inside the Business Vault. This article focuses on using the data from the combined Data Vault model (that is, both the Raw Data Vault and Business Vault) and transforming it into valuable information to provide to business users. \n Information Delivery \n The Raw Data Vault and Business Vault capture the raw data from the source systems and the results from the business logic required by the business users. One could argue that the job is done by then. \n \n But in reality, end-users typically don’t want to work with the Raw Data Vault or Business Vault entities. The valid reasons for that often include a lack of knowledge in Data Vault modeling and, hence, unclarity about how to query a Data Vault implementation. Additionally, most end-users are already familiar with different data consumption methods. This typically includes dimensional models, such as star schema or snowflake schema, or fully denormalized flat-and-wide tables. In this article, we discuss fact entities and slowly changing dimensions (SCD) 1 and 2. \n \n Also, most tools for delivering information, such as dashboarding tools like Microsoft PowerBI or SQL Server Analysis Services to produce OLAP cubes, are easy to use with such models. \n How to Deliver Information with Data Vault 2.0 \n Regardless of the desired information delivery format, it can be directly queried out of Raw Data Vault entities. The Data Vault model follows an optimized schema-on-read design where the raw data is stored as-is and transformations, such as business logic and structural changes, are applied during query time. This is true, except that the incoming source data is broken down into the fundamental components: business keys, relationships, and descriptive data. This is the optimization of the storage and it makes the application of business rules much easier and also the transformation into any desired target information schema. \n \n Business Vault entities are used during information delivery to apply business rules. In most cases, the raw data is insufficient for reporting: it contains erroneous data, and some data is missing or needs to be converted from one currency to another. However, some of the raw data is good enough for reporting. Therefore, in many cases, information models, such as a dimensional model, would be derived from both the Raw Data Vault and the Business Vault by joining the required entities. \n \n Information Delivery requirements typically include a historization requirement. A Slowly Changing Dimension (SCD) Type 1 would only include the current state of descriptive attributes. However, SCD Type 2 would consist of the full history of descriptive attributes. Data Vault follows a multi-temporal approach and leverages multiple timelines to implement such solutions: \n \n \n The load date timestamp is the technical timeline that indicates when data arrived at the data platform. The timeline must be defined (and controlled) by the data platform team. \n The snapshot timestamp indicates when information should be delivered to the end user. This timeline is regular (e.g., every morning at 8 a.m.) and defined by the business user. \n Business timelines are inside the source data and indicate when something happened. Examples include birth dates, valid from and to dates, change dates, and deletion dates. \n \n \n Separating these timelines and creating multi-temporal solutions, where some data is back-dated or post-dated, becomes much more straightforward. However, this is beyond the scope of this article. \n Implementation Walk Through \n To fulfill the business requirements, let's start as simple as possible. For various reasons, it’s highly recommended that information marts be implemented using SQL views initially and only use physical tables if performance or processing times/costs require it. Other options like PIT and bridge tables typically provide a sufficient (virtualized) solution. \n \n We follow this recommendation in this article and start with a dimension view and a fact view. \n Store Dimension \n \n Many dimensions entities are derived from a hub and its satellite. If no business rules are implemented, the Dimension can access directly from the Raw Data Vault entities. For example, the following CREATE VIEW statement implements a SCD Type 1 store dimension: \n \n CREATE VIEW InformationMarts.DIM_STORE_SCD1 AS\n\n\n\nSELECT\n\n hub.store_hashkey as StoreKey,\n\n hub.store_id as StoreID,\n\n sat.address_street as AddressStreet,\n\n sat.postal_code as PostalCode,\n\n sat.country as Country\n\nFROM DV.store_hub hub\n\nLEFT JOIN DV.store_address_crm_lroc_sat sat\n\n ON hub.hk_store_hub = sat.hk_store_hub\n\nWHERE sat.is_current = 1 \n \n This simple query accesses the store_hub and joins it to the store_address satellite. It selects the business key from the hub because typical business users want to include it in the dimension. In addition, it renames all descriptive attributes from the satellite to make them more readable. The hash key is added for efficient joins from Fact entities. In the end, a WHERE clause leverages the is_current flag in the satellite to only include the latest descriptive data. This flag is calculated in a view on top of the actual satellite table. Thus, the view is joined, not the table. Only this specific WHERE clause makes this dimension of SCD type 1. Leaving it away would automatically lead to an SCD type 2! However, in such a case, it would make sense to include the load_date and load_end_date of the satellite view additionally. \n Transaction Fact \n The following CREATE VIEW statement implements a fact entity. In this simple example, no aggregations are defined. The granularity of the derived fact entity matches the underlying data from the non-historized link. Therefore, the fact view can be directly derived from the non-historized link without the requirement for a grain shift, e.g., a GROUP BY clause: \n \n CREATE VIEW InformationMarts.FACT_STORE_TRANSACTIONS AS\n\n\n\nSELECT\n\n nl.transaction_id as TransactionID,\n\n s_hub.store_hashkey as StoreKey,\n\n c_hub.customer_hashkey as CustomerKey,\n\n nl.transaction_date as TransactionDate,\n\n nl.amount as Amount\n\nFROM DV.store_transaction_nlnk nl\n\nLEFT JOIN DV.store_hub s_hub\n\n ON nl.hk_store_hub = s_hub.hk_store_hub\n\nLEFT JOIN DV.customer_hub c_hub\n\n ON nl.hk_customer_hub = c_hub.hk_customer_hub \n \n This query selects from the non-historized link and joins both hubs via the hashkeys. From these hubs, the hash keys are assigned. From the non-historized link, the relevant transaction details are chosen. A filter for historization is not required because both hubs and non-historized links only capture non-changing data. Capturing changing facts, which in theory should never happen but might happen in reality, is also possible using non-historized links but beyond the scope of this article. \n Pre-Calculated Aggregations \n In most business environments, BI developers would now connect their reporting tool of choice against our provided dimensional model to create custom reports. It's common to aggregate data to calculate sums, counts, averages, or other aggregated values, especially for fact data. Depending on the data volume, the reporting tool, and the aggregation complexity, this might be a challenge for business users. \n \n To simplify usage and optimize query performance in some cases, a pre-aggregation in the dimensional layer might be the best choice. For example, the CREATE VIEW statement implements another store transaction fact view that already includes the requested aggregations. Since aggregations are always based on a GROUP BY clause, the following views implement both grain shifts to calculate the number and amount of transactions on the different dimensions of store and customer: \n \n CREATE VIEW InformationMarts.FACT_AGG_STORE_TRANSACTIONS AS\n\n\n\nSELECT\n\n s_hub.store_hashkey as StoreKey,\n\n COUNT(nl.transaction_id) as TransactionCount,\n\n SUM(nl.amount) as TotalAmount,\n\n AVG(nl.amount) as AverageAmount\n\nFROM DV.store_transaction_nlnk nl\n\nLEFT JOIN DV.store_hub s_hub\n\n ON nl.hk_store_hub = s_hub.hk_store_hub\n\nGROUP BY s_hub.store_hashkey \n \n CREATE VIEW InformationMarts.FACT_AGG_CUSTOMER_TRANSACTIONS AS\n\n\n\nSELECT\n\n c_hub.customer_hashkey as CustomerKey,\n\n COUNT(nl.transaction_id) as TransactionCount,\n\n SUM(nl.amount) as TotalAmount,\n\n AVG(nl.amount) as AverageAmount\n\nFROM DV.store_transaction_nlnk nl\n\nLEFT JOIN DV.customer_hub c_hub\n\n ON nl.hk_customer_hub = c_hub.hk_customer_hub\n\nGROUP BY c_hub.customer_hashkey \n \n In both these queries, only one hub is required. The hash key of each hub is used for the GROUP BY clause, and three basic aggregations are applied to determine the count of transactions and calculate the sum and average amount of transactions. \n \n While this reduces the workload on the business user side, this implementation might still be slow or produce high processing costs. So, it would make sense to start materializing this aggregated fact entity or to introduce a bridge table. \n \n A bridge table is similar to a pre-aggregated fact table in dimensional models. However, it is much more customizable as it only implements the grain shift operation (in this case, the GROUP BY clause), measure calculations, and timelines. It also contains the hub references, which will be turned into dimension references, as seen in the previous examples. \n \n The definition of the bridge table is provided in the following statement: \n \n CREATE TABLE [DV].[CUSTOMER_TRANSACTIONS_BB] (\n\n SnapshotDate DATETIME2(7) NOT NULL,\n CustomerKey CHAR(32) NOT NULL,\n\n TransactionCount BIGINT NOT NULL,\n\n AverageAmount MONEY NOT NULL\n\n); \n \n The code to load a bridge table is similar to the fact view: \n \n INSERT INTO [DV].[CUSTOMER_TRANSACTIONS_BB]\nSELECT\n\n SYSDATETIME() as SnapshotDate\n\n nl.customer_hashkey as CustomerKey,\n\n COUNT(nl.transaction_id) as TransactionCount,\n\n SUM(nl.amount) as TotalAmount,\n\n AVG(nl.amount) as AverageAmount\n\nFROM DV.store_transaction_nlnk nl\n\nGROUP BY nl.customer_hashkey; \n \n The bridge table might also contain complex business calculations in many other cases. Still, the focus is on the grain shift operation, which takes a reasonable amount of time on many traditional database systems due to their row-based storage. However, Microsoft Fabric uses a different storage format optimized for aggregations but typically at the price of joins. \n \n The bridge table aims to improve the query performance of fact entities. In turn, that means it is ok to pre-join other data into the bridge table if the join performance is insufficient. \n \n A common requirement is the addition of a time dimension. \n Snapshot-Based Information Delivery \n So far, the store dimension presented in this article was an SCD Type 1 dimension - a dimension without history. However, in many cases, businesses want to relate facts to the dimension’s member version of the time the fact occurred. For example, an order was issued before the customer relocated to another state. In a Type 1 scenario, the order’s revenue would be associated with the customer's current state. However, this might not be correct, depending on the information requirements. In such cases, the revenue should be associated with the customer's state at the time of the transaction. \n \n This information requirement demands an SCD Type 2 dimension with history. Point-in-time (PIT) tables are recommended to produce such dimensions efficiently. This section discusses the necessary steps to create such a table. \n \n A good starting point is a date table. This table is a reference table for dates and can produce a date dimension and populate the PIT table. The following statement creates the table and initializes it with dates between 1970 and 2099: \n \n CREATE SCHEMA CONTROL;\n\n\n\nCREATE TABLE CONTROL.Ref_Date_v0\n\n (snapshot_datetime datetime2(6),\n\n snapshot_date date,\n\n year int,\n\n month int,\n\n quarter int,\n\n week int,\n\n day int,\n\n day_of_year int,\n\n week_day int,\n\n beginning_of_year bit,\n\n beginning_of_quarter bit,\n\n beginning_of_month bit,\n\n beginning_of_week bit,\n\n end_of_year bit,\n\n enf_of_quarter bit,\n\n end_of_month bit,\n\n end_of_week bit)\n\n\n\n\n\nWITH\n\n \n\ndate_base AS (\n\n\n\n SELECT n\n\n FROM (VALUES (0),(1),(2),(3),(4),(5),(6),(7),(8),(9)) v(n)\n\n \n\n ),\n\n\n\ndate_basic as (\n\nSELECT TOP (DATEDIFF(DAY, '1970-01-01', '2099-12-31') + 1)\n\n ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) - 1 AS rn\n\nFROM date_base ones, date_base tens, date_base hundreds, date_base thousands\n\nORDER BY 1\n\n\n\n),\n\n\n\nsnapshot_base AS (\n\n \n\n select\n\n cast(dateadd(day, rn - 1, '2020-01-01 07:00:00') as datetime2) as snapshot_datetime,\n\n cast(dateadd(day, rn - 1, '2020-01-01') as date) as snapshot_date\n\n from date_basic\n\n\n\n ),\n\n\n\nsnapshot_extended AS (\n\n\n\n SELECT\n\n snapshot_datetime,\n\n snapshot_date,\n\n DATEPART(YEAR, snapshot_date) as year,\n\n DATEPART(MONTH, snapshot_date) as month,\n\n DATEPART(QUARTER, snapshot_date) as quarter,\n\n DATEPART(WEEK, snapshot_date) as week,\n\n DATEPART(DAY, snapshot_date) as day,\n\n DATEPART(DAYOFYEAR, snapshot_date) as day_of_year,\n\n DATEPART(WEEKDAY, snapshot_date) as week_day\n\n FROM snapshot_base\n\n\n\n)\n\n\n\nINSERT INTO CONTROL.Ref_Date_v0\n\n\n\n SELECT\n\n *,\n\n CASE WHEN day_of_year = 1 THEN 1 ELSE 0 END as beginning_of_year,\n\n CASE WHEN day = 1 AND month in (1, 4, 7, 10) THEN 1 ELSE 0 END as beginning_of_quarter,\n\n CASE WHEN day = 1 THEN 1 ELSE 0 END as beginning_of_month,\n\n CASE WHEN week_day = 2 THEN 1 ELSE 0 END as beginning_of_week,\n\n CASE WHEN snapshot_date = EOMONTH(snapshot_date) AND month = 12 THEN 1 ELSE 0 END as end_of_year,\n\n CASE WHEN snapshot_date = EOMONTH(snapshot_date) AND month in (3, 6, 9, 12) THEN 1 ELSE 0 END as end_of_quarter,\n\n CASE WHEN snapshot_date = EOMONTH(snapshot_date) THEN 1 ELSE 0 END as end_of_month,\n\n CASE WHEN week_day = 1 THEN 1 ELSE 0 END as end_of_week\n\n\n\n FROM snapshot_extended \n \n The first part is a simple DDL statement that creates the reference date table. This is followed by an INSERT statement that leverages multiple Common Table Expressions (CTEs) to simplify the logic. The first CTE date_base simply generates a list of the numbers 1 to 10, followed by the CTE date_basic, which CROSS JOINs the previous CTE four times, creating 10 * 10 * 10 * 10 = 10000 rows. A ROW_NUMBER() transforms the numbers into an ascending number ranging from 1 to 10000. The next CTE snapshot_base uses this ascending number to execute a DATEADD() function on top of a specified start date, '2020-01-01 07:00:00', to generate a list of daily dates. This is done once in datatype datetime2 and once in datatype date. The last CTE snapshot_extended adds metadata like MONTH, YEAR, etc. Lastly, boolean columns, which mark the beginning and end of weeks, months, quarters, and years., are added. Everything is then inserted into the reference date table. \n \n This reference date table can now create and load a Point-In-Time (PIT) Table. The PIT table precalculates for each snapshot date timestamp (SDTS), which satellite entry is valid for each business key. The granularity of a PIT is (number of snapshots) * (number of business keys) = row count in PIT. The following code creates and populates a simple PIT example for stores: \n \n CREATE TABLE DV.STORE_BP\n\n(hk_d_store CHAR(32) NOT NULL,\n\nhk_store_hub CHAR(32) NOT NULL,\n\nsnapshot_datetime datetime2(6) NOT NULL,\n\nhk_store_address_crm_lroc_sat CHAR(32) NULL,\n\nload_datetime_store_address_crm_lroc_sat datetime2(6) NULL);\n\n\n\nWITH pit_entries AS (\n\nSELECT\n\n CONVERT(CHAR(32), HASHBYTES('MD5', CONCAT(hub.hk_store_hub, '||', date.snapshot_datetime)), 2) as hk_d_store,\n\n hub.hk_store_hub,\n\n date.snapshot_datetime,\n\n COALESCE(sat1.hk_store_hub, '00000000000000000000000000000000') as hk_store_address_crm_lroc_sat,\n\n COALESCE(sat1.load_datetime, CONVERT(DATETIME, '1900-01-01T00:00:00', 126)) as load_datetime_store_address_crm_lroc_sat\n\n\n\nFROM DV.store_hub hub\n\nINNER JOIN CONTROL.Ref_Date_v0 date\n\n ON hub.load_datetime <= date.snapshot_datetime\n\nLEFT JOIN DV.store_address_crm_lroc_sat sat1\n\n ON hub.hk_store_hub = sat1.hk_store_hub\n\n AND date.snapshot_datetime BETWEEN sat1.load_datetime and sat1.load_end_datetime\n\n\n\n )\n\n\n\nINSERT INTO DV.STORE_BP\n\nSELECT\n\n*\n\nFROM pit_entries new\n\nWHERE NOT EXISTS (SELECT 1 FROM DV.STORE_BP pit\n\n WHERE pit.hk_d_store = new.hk_d_store) \n \n The one CTE pit_entries defines the whole set of PIT entries. The store hub is joined against the snapshot table only when the hub appears before the SDTS to reduce the number of rows. But since there is no more specific JOIN condition, after this join, the number of rows is already a multiple of the number of rows in the hub. \n \n Next, the only satellite attached to the store hub is joined, store_address_crm_lroc_sat. It is joined on the hash key, and additionally, the load_date and load_end_date are leveraged to determine the valid record for a specific SDTS using the BETWEEN function. \n \n The SELECT list of this CTE introduces a new concept, a dimensional key, hk_d_store, generated by hashing the store hub hash key and the SDTS. This creates a new unique column that can be used for the primary key constraint and incremental loads. Additionally, both components of this dimensional key, hk_store_hub and snapshot_datetime, are selected. The hash key and load, datetime of the satellite, are also chosen to uniquely identify one row of the satellite. They are renamed to include the satellite's name, which helps when joining multiple satellites instead of just one. A typical PIT always brings together all satellites connected to a specific hub. Therefore, a typical PIT has various combinations of hash key and load_datetime columns. \n \n Ultimately, we insert only rows where the new dimensional key does not already exist in the target PIT. This additional clause enables incremental loading. \n \n This PIT can now be used as a starting point for a snapshot-based store dimension. To produce a historized (SCD Type 2) store dimension, the PIT is joined with the hub and the satellite: \n CREATE VIEW InformationMarts.DIM_STORE_SB AS\n\n\n\nSELECT\n\n pit.snapshot_datetime as SnapshotDatetime,\n\n hub.store_id as StoreID,\n\n sat.address_street as AddressStreet,\n\n sat.postal_code as PostalCode,\n\n sat.country as Country\n\nFROM DV.STORE_BP pit\n\nINNER JOIN DV.store_hub hub\n\n ON hub.hk_store_hub = pit.hk_store_hub\n\nINNER JOIN DV.store_address_crm_lroc_sat sat\n\n ON pit.hk_store_address_crm_lroc_sat = sat.hk_store_hub\n\n AND pit.load_datetime_store_address_crm_lroc_sat = sat.load_datetime \n \n With all history precalculated in our PIT, the actual dimension can be virtual again because the only operation required is an INNER-JOIN. Additional information and patterns about PIT and bridge tables can be found on the Scalefree Blog. \n Conclusion \n Data Vault has been designed to integrate data from multiple data sources, creatively destruct the data into its fundamental components, and store and organize it so that any target structure can be derived quickly. This article focused on generating information models, often dimensional models, using virtual entities. They are used in the data architecture to deliver information. After all, dimensional models are easier to consume by dashboarding solutions, and business users know how to use dimensions and facts to aggregate their measures. However, PIT and bridge tables are usually needed to maintain the desired performance level. They also simplify the implementation of dimension and fact entities and, for those reasons, are frequently found in Data Vault-based data platforms. \n \n This article completes the information delivery. The following articles will focus on the automation aspects of Data Vault modeling and implementation. \n \n <<< Back to Blog Series Title Page ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"20928","kudosSumWeight":0,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzkwNTc4LXVOQndybw?revision=1\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzkwNTc4LXVOQndybw?revision=1"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4398162":{"__typename":"Conversation","id":"conversation:4398162","topic":{"__typename":"BlogTopicMessage","uid":4398162},"lastPostingActivityTime":"2025-03-27T12:51:08.271-07:00","solved":false},"User:user:873656":{"__typename":"User","uid":873656,"login":"Khushhalgarg","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-8.svg?time=0"},"id":"user:873656"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLTRSTllXNw?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLTRSTllXNw?revision=2","title":"clipboard_image-1-1743104660973.png","associationType":"BODY","width":1164,"height":418,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLWFEY3dYQw?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLWFEY3dYQw?revision=2","title":"image.png","associationType":"BODY","width":1783,"height":777,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLXkwY200YQ?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLXkwY200YQ?revision=2","title":"image.png","associationType":"BODY","width":1782,"height":778,"altText":""},"BlogTopicMessage:message:4398162":{"__typename":"BlogTopicMessage","subject":"Creating a AI-Driven Chatbot to Inquire Insights into business data","conversation":{"__ref":"Conversation:conversation:4398162"},"id":"message:4398162","revisionNum":2,"uid":4398162,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:873656"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"A Comprehensive Guide Utilizing Exported Data from D365 F&O","metrics":{"__typename":"MessageMetrics","views":409},"postTime":"2025-03-27T12:51:08.271-07:00","lastPublishTime":"2025-03-27T12:51:08.271-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n In the fast-paced digital era, the ability to extract meaningful insights from vast datasets is paramount for businesses striving for a competitive edge. Microsoft Dynamics 365 Finance and Operations (D365 F&O) is a robust ERP platform, generating substantial business data. To unlock the full potential of this data, integrating it with advanced analytics and AI tools such as Azure OpenAI, Azure Synapse Workspace, or Fabric Workspace is essential. This blog will guide you through the process of creating a chatbot to inquire insights using Azure OpenAI with Azure Synapse Workspace or Fabric Workspace. \n Architecture \n \n \n Natural Language Processing (NLP): Enables customers to inquire about business data such as order statuses, item details, and personalized order information using natural language. \n Seamless Data Integration: Real-time data fetching from D365 F&O for accurate and up-to-date information. \n Contextual and Personalized Responses: AI provides detailed, context-rich responses to customer queries, improving engagement and satisfaction. \n Scalability and Efficiency: Handles multiple concurrent inquiries, reducing the burden on customer service teams and improving operational efficiency. \n \n \n Understanding the Components \n Microsoft Dynamics 365 Finance and Operations (D365 F&O) \n D365 F&O is a comprehensive ERP solution designed to help businesses streamline their operations, manage finances, and control supply chain activities. It generates and stores vast amounts of transactional data essential for deriving actionable insights. \n Dataverse \n Dataverse is a cloud-based data storage solution that allows you to securely store and manage data used by business applications. It provides a scalable and reliable platform for data integration and analytics, enabling businesses to derive actionable insights from their data. \n Azure Synapse Analytics \n Azure Synapse Analytics is an integrated analytics service that brings together big data and data warehousing. It allows users to query data on their terms, deploying either serverless or provisioned resources at scale. The service provides a unified experience to ingest, prepare, manage, and serve data for instant business intelligence and machine learning requirements. \n \n Fabric Workspace \n Fabric Workspace provides a collaborative platform for data scientists, analysts, and business users to work together on data projects. It facilitates the seamless integration of various data sources and advanced analytics tools to drive innovative solutions. \n \n Azure SQL Database \n Azure SQL Database is a cloud-based relational database service built on Microsoft SQL Server technologies. It offers a range of deployment options, including single databases, elastic pools, and managed instances, allowing you to choose the best fit for your application needs. Azure SQL Database provides high availability, scalability, and security features, making it an ideal choice for modern applications. Data from Dynamics 365 Finance and Operations (F&O) is copied to an Azure SQL Database using a flow that involves Azure Data Lake Storage (ADLS) and Azure Data Factory (ADF) \n Azure OpenAI \n Azure OpenAI enables developers to build and deploy intelligent applications using powerful AI models. By integrating OpenAI’s capabilities with Azure’s infrastructure, businesses can create sophisticated solutions that leverage natural language processing, machine learning, and advanced analytics. \n Step-by-Step Guide to Creating the Chatbot \n Step 1: Export Data from D365 F&O \n To begin, export the necessary data from your D365 F&O instance. This data will serve as the foundation for your analytics and AI operations. Ensure the exported data is in a format compatible with Azure Synapse or Fabric Workspace. \n Step 2: Ingest Data into Azure Synapse Workspace or Fabric Workspace \n Next, ingest the exported data into Azure Synapse Workspace or Fabric Workspace. Utilize the workspace’s capabilities to prepare, manage, and optimize the data for further analysis. This step involves setting up data pipelines, cleaning the data, and transforming it into a suitable format for processing. \n Step 3: Set Up Azure OpenAI \n With your data ready, set up Azure OpenAI in your environment. This involves provisioning the necessary resources, configuring the OpenAI service, and integrating it with your Azure infrastructure. Ensure you have the appropriate permissions and access controls in place. \n Step 4: Develop the Chatbot \n Develop the chatbot using Azure OpenAI’s capabilities. Design the chatbot to interact with users naturally, allowing them to inquire insights and receive valuable information based on the data from D365 F&O. Utilize natural language processing to enhance the chatbot’s ability to understand and respond to user queries effectively. \n Step 5: Integrate the Chatbot with Azure Synapse or Fabric Workspace \n Integrate the developed chatbot with Azure Synapse Workspace or Fabric Workspace. This integration will enable the chatbot to access and analyze the ingested data, providing users with real-time insights. Set up the necessary APIs and data connections to facilitate seamless communication between the chatbot and the workspace. \n Step 6: Test and Refine the Chatbot \n Thoroughly test the chatbot to ensure it functions as expected. Address any issues or bugs, and refine the chatbot’s responses and capabilities. This step is crucial to ensure the chatbot delivers accurate and valuable insights to users. \n Best Practices for Data Access \n Data Security \n Data security is paramount when exporting sensitive business information. Implement the following best practices: \n \n Ensure that all data transfers are encrypted using secure protocols. \n Use role-based access control to restrict access to the data exported. \n Regularly audit and monitor data export activities to detect any unauthorized access or anomalies. \n \n Data Transformation \n Transforming data before accessing it can enhance its usability for analysis: \n \n Use Synapse data flows to clean and normalize the data. \n Apply business logic to enrich the data with additional context. \n Aggregate and summarize data to improve query performance. \n \n Monitoring and Maintenance \n Regular monitoring and maintenance ensure the smooth operation of your data export solution: \n \n Set up alerts and notifications for any failures or performance issues in the data pipelines. \n Regularly review and optimize the data export and transformation processes. \n Keep your Azure Synapse environment up to date with the latest features and enhancements. \n \n Benefits of Integrating AI and Advanced Analytics \n Enhanced Decision-Making \n By leveraging AI and advanced analytics, businesses can make data-driven decisions. The chatbot provides timely insights, enabling stakeholders to act quickly and efficiently. \n Improved Customer Experience \n A chatbot enhances customer interactions by providing instant responses and personalized information. This leads to higher satisfaction and engagement levels. \n Operational Efficiency \n Integrating AI tools with business data streamlines operations, reduces manual efforts, and increases overall efficiency. Businesses can optimize processes and resource allocation effectively. \n Scalability \n It can handle multiple concurrent inquiries, scaling as the business grows without requiring proportional increases in customer service resources. \n Conclusion \n Creating a chatbot to inquire insights using Azure OpenAI with Azure Synapse Workspace or Fabric Workspace represents a significant advancement in how businesses can leverage their data. By following the steps outlined in this guide, organizations can develop sophisticated AI-driven solutions that enhance decision-making, improve customer experiences, and drive operational efficiency. Embrace the power of AI and advanced analytics to transform your business and unlock new opportunities for growth. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"8029","kudosSumWeight":1,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLTRSTllXNw?revision=2\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLWFEY3dYQw?revision=2\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTYyLXkwY200YQ?revision=2\"}"}}],"totalCount":3,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4397140":{"__typename":"Conversation","id":"conversation:4397140","topic":{"__typename":"BlogTopicMessage","uid":4397140},"lastPostingActivityTime":"2025-03-26T19:27:17.907-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLXpFeXl4aA?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLXpFeXl4aA?revision=8","title":"image.png","associationType":"BODY","width":358,"height":80,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLUR2WTVIRQ?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLUR2WTVIRQ?revision=8","title":"1742998745484.gif","associationType":"BODY","width":1440,"height":1440,"altText":""},"BlogTopicMessage:message:4397140":{"__typename":"BlogTopicMessage","subject":"Anthropic State-of-the-Art Models Available to Azure Databricks Customers","conversation":{"__ref":"Conversation:conversation:4397140"},"id":"message:4397140","revisionNum":8,"uid":4397140,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:201631"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"We are thrilled to introduce even more model choices with the arrival of Anthropic Claude 3.7 Sonnet in Azure Databricks!\n","metrics":{"__typename":"MessageMetrics","views":3938},"postTime":"2025-03-26T07:11:14.886-07:00","lastPublishTime":"2025-03-26T19:27:17.907-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Our customers now have greater model choices with the arrival of Anthropic Claude 3.7 Sonnet in Azure Databricks. Databricks is announcing a partnership with Anthropic to integrate their state-of-the-art models into Databricks Data Intelligence Platform as a native offering, starting with Claude 3.7 Sonnet http://databricks.com/blog/anthropic-claude-37-sonnet-now-natively-available-databricks. With this announcement, Azure customers can use Claude Models directly in Azure Databricks. Foundation model REST API reference - Azure Databricks | Microsoft Learn \n With Anthropic models available in Azure Databricks, customers can use the Claude \"think\" tool with business data optimized promote to guide Claude efficiently perform complex tasks. With Claude models in Azure Databricks, enterprises can deliver domain-specific, high quality AI agents more efficiently. As an integrated component of the Azure Databricks Data Intelligence Platform, Anthropic Claude models benefit from comprehensive end-to-end governance and monitoring throughout the entire data and AI lifecycle with Unity Catalog. \n With Claude models, we remain committed to providing customers with model flexibility. Through the Azure Databricks Data Intelligence Platform, customers can securely connect to any model provider and select the most suitable model for their needs. They can further enhance these models with enterprise data to develop domain-specific, high-quality AI agents, supported by built-in custom evaluation governance across both data and models. \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1567","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLXpFeXl4aA?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MTQwLUR2WTVIRQ?revision=8\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3876206":{"__typename":"Conversation","id":"conversation:3876206","topic":{"__typename":"BlogTopicMessage","uid":3876206},"lastPostingActivityTime":"2025-03-07T01:42:23.453-08:00","solved":false},"BlogTopicMessage:message:3876206":{"__typename":"BlogTopicMessage","subject":"Data Vault 2.0 Warehouse Automation on Azure","conversation":{"__ref":"Conversation:conversation:3876206"},"id":"message:3876206","revisionNum":13,"uid":3876206,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:1741331"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This is the series of 'Blog Articles' on the topic \"Data Vault 2.0 on Azure\" where we start from 'What?' and then slowly dwell into 'How To?' implement DV 2.0 on Azure Data Platform Technologies. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":8296},"postTime":"2023-07-17T07:02:28.380-07:00","lastPublishTime":"2025-03-07T01:42:23.453-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" This is the series of 'Blog Articles' on the topic \"Data Vault 2.0 on Azure\" where we start from 'What?' and then slowly dwell into 'How To?' implement DV 2.0 on Azure Data Platform Technologies. \n \n \n A Modern Data Analytics Platform on Azure with Data Vault 2.0 \n Data Vault 2.0 on Azure \n Real-Time Processing with Data Vault 2.0 on Azure \n Data Vault 2.0 using Databricks Lakehouse Architecture on Azure \n Data Science with Azure Synapse and Data Vault 2.0 \n Agile Data Vault 2.0 Projects with Azure DevOps \n Data Vault 2.0 Modelling in Synapse Data Warehouse on Microsoft Fabric \n \n Advanced Data Vault 2.0 Models on Synapse Data Warehouse in Microsoft Fabric \n \n Modelling Microsoft Dynamics 365 Data Using Data Vault 2.0 \n Implementing Data Vault 2.0 on Fabric Data Warehouse \n Implementing Business Logic using Data Vault 2.0 on Azure Fabric \n Delivering Information with Azure Synapse and Data Vault 2.0 \n \n \n These series of blogs are done in partnership with our Data Warehouse Automation Partners Vaultspeed and Scalefree. \n \n These Article are written by Vaultspeed and Scalefree Professionals and Technical Review is done by Microsoft GBB Team for EMEA (Cloud Scale Analytics). \n \n About the Authors in Blog Series (Scalefree and VaultSpeed) \n Scalefree Team \n \n Michael Olschimke is co-founder and CEO at Scalefree International GmbH, a Big-Data consulting firm in Europe, empowering clients across all industries to take advantage of Data Vault 2.0 and similar Big Data solutions. Michael has trained thousands of data warehousing individuals from the industry, taught classes in academia, and publishes on these topics on a regular basis. \n Marc Winkelmann is working in Business Intelligence and Enterprise Data Warehousing (EDW) with a focus on Data Vault 2.0 implementation and coaching. Since 2016 he is active in consulting and implementation of Data Vault 2.0 solutions with industry leaders in manufacturing, energy supply and facility management sector. In 2020 he became a Data Vault 2.0 Instructor for Scalefree. \n Ole Bause is working at Scalefree in the area of Business Intelligence, Data Engineering, and Enterprise Data Warehousing with Data Vault 2.0. He is a certified Data Vault 2.0 Practitioner and Azure Data Engineer Associate. Data warehouse automation is also one of his core areas of competence. \n Tim Kirschke is working at Scalefree in the area of Business Intelligence, Data Engineering, and Enterprise Data Warehousing with Data Vault 2.0 \n Dymtro Polishchuk is a Senior BI Consultant for Scalefree. He has 6 years of experience in Business Intelligence. In his career Dmytro was able to gain extensive knowledge in the areas of ETL development and data warehousing including Data Vault 2.0 on Microsoft technologies such as SQL Server, Azure and Power BI. \n \n \n VaultSpeed Team \n \n Jonas De Keuster is VP Product Marketing at VaultSpeed. He had close to 10 years of experience as a DWH consultant in various industries like banking, insurance, healthcare, and HR services, before joining the data automation vendor. This background allows him to help understand current customer needs and engage in conversations with members of the data industry. \n Koen Moermans is responsible for professional services at VaultSpeed, the leading data warehouse automation platform. Koen works daily with clients from various industries to help them build their data warehouse with Data Vault and VaultSpeed. Koen has a vast experience in data warehousing on all leading data platforms, especially on Azure Synapse and Databricks. \n \n \n About the Technical Reviewers in Blog Series - Microsoft GBB (EMEA) Team for Cloud Scale Analytics \n \n Ian Clarke is a GBB for Cloud Scale Analytics (EMEA) at Microsoft. \n Naveed Hussain is a GBB for Cloud Scale Analytics (EMEA) at Microsoft. \n Irfan Maroof is a GBB for Cloud Scale Analytics (EMEA) at Microsoft. \n Hajar Habjaoui is a GBB for Cloud Scale Analytics (EMEA) at Microsoft. \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"4093","kudosSumWeight":0,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4377517":{"__typename":"Conversation","id":"conversation:4377517","topic":{"__typename":"BlogTopicMessage","uid":4377517},"lastPostingActivityTime":"2025-02-12T06:47:32.193-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzc3NTE3LVRQbXlCTA?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzc3NTE3LVRQbXlCTA?revision=1","title":"image.png","associationType":"BODY","width":975,"height":657,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzc3NTE3LXM5YXRTRQ?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzc3NTE3LXM5YXRTRQ?revision=1","title":"image.png","associationType":"BODY","width":975,"height":646,"altText":""},"BlogTopicMessage:message:4377517":{"__typename":"BlogTopicMessage","subject":"Workarounds for Maven Json-smart 2.5.2 Release Breaking Azure Databricks Job Dependencies","conversation":{"__ref":"Conversation:conversation:4377517"},"id":"message:4377517","revisionNum":1,"uid":4377517,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:201631"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"A recent Maven library release corrupted json-smart’s central metadata, ended up removed all previous versions of Maven library other than 2.5.2. To mitigate the issue, you can take the workaround steps to install the customer’s requested library and smart-json separately","metrics":{"__typename":"MessageMetrics","views":1014},"postTime":"2025-02-12T06:47:32.193-08:00","lastPublishTime":"2025-02-12T06:47:32.193-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" A recent Maven library release corrupted json-smart’s central metadata, ended up removing all previous versions of Maven library other than 2.5.2. This results in DBR ivy resolution failures if customers’ job has transitive dependency on previous version of json-smart. The impact to Azure Databricks customers is jobs failure and in some cases the job clusters could fail to start. More details and updates of the issue can be found here: https://github.com/netplex/json-smart-v2/issues/240. \n This also affects google’s maven mirror, which is used by DBR 11+ to resolve maven libraries, and maven central is used as the backup for google’s maven mirror. \n To mitigate the issue, you can take the workaround steps to install the customer’s requested library and smart-json separately \n Steps 1: Install the customer’s requested library (use azure-eventhubs-spark as an example) but exclude json-smart. \n \n Step 2: Install net.minidev:json-smart:2.3 (or any other version that was needed) \n \n Workaround for run time, if you have a preferred Maven mirror that is not affected by this issue, or if you host a private Maven mirror, you can configure your Databricks environment to use it. \n \n Step: \n \n Set the Spark configuration parameter spark.databricks.driver.preferredMavenCentralMirrorUrl to your preferred mirror repository URL. \n \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1384","kudosSumWeight":1,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzc3NTE3LVRQbXlCTA?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzc3NTE3LXM5YXRTRQ?revision=1\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4066997":{"__typename":"Conversation","id":"conversation:4066997","topic":{"__typename":"BlogTopicMessage","uid":4066997},"lastPostingActivityTime":"2025-01-14T21:46:27.322-08:00","solved":false},"User:user:1307255":{"__typename":"User","uid":1307255,"login":"ApurbaSR","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-11.svg?time=0"},"id":"user:1307255"},"BlogTopicMessage:message:4066997":{"__typename":"BlogTopicMessage","subject":"Securing Azure HDInsight: ESM Support with Ubuntu 18.04, Cluster Updates, and Best Practices","conversation":{"__ref":"Conversation:conversation:4066997"},"id":"message:4066997","revisionNum":9,"uid":4066997,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:1307255"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":2155},"postTime":"2024-02-27T07:35:08.218-08:00","lastPublishTime":"2025-01-14T21:46:27.322-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Azure HDInsight, Microsoft's cloud-based big data analytics platform, continues to advance its features to provide users with a secure and efficient environment. In this article, we will explore the latest enhancements, focusing on Expanded Security Maintenance (ESM) support, the importance of regular cluster updates, and best practices recommended by Microsoft to fortify HDInsight deployments. \n \n The foundation of a secure Azure HDInsight environment lies in its ability to address critical vulnerabilities promptly. Microsoft ensures this by shipping the latest HDInsight images with Expanded Security Maintenance (ESM) support, which provides a framework for ongoing support, stability with minimal changes specifically targeting critical, high and some medium-level fixes. This ensures that HDInsight users benefit from a continuously updated and secure environment. \n \n \n ESM Support in Latest Images: Azure HDInsight 5.0 and 5.1 versions use Ubuntu 18.04 pro image. Ubuntu Pro includes security patching for all Ubuntu packages due to Expanded Security Maintenance (ESM) for Infrastructure and Applications. Ubuntu Pro 18.04 LTS will remain fully supported until April 2028. For more information on what's new in the latest HDInsight images with ESM support, users can refer to the official release notes on the Azure HDInsight Release Notes Archive. \n \n \n \n Periodic Cluster Updates: Maintaining a secure HDInsight environment requires diligence in keeping clusters up to date. Microsoft facilitates this process through the HDInsight OS patching mechanism. Periodically updating clusters using the procedures outlined in the official documentation ensures that users benefit from the latest features, performance improvements, and crucial security patches. Learn more about updating HDInsight clusters through the Azure HDInsight OS Patching documentation. \n \n \n \n ESM and HDI Release Integration: Extended Security Maintenance is seamlessly integrated into HDInsight releases. As part of each HDInsight release, critical fixes provided by ESM are bundled. This ensures that users benefit from the latest security enhancements with each new release. \n \n \n \n Customer Recommendation: Use the Latest Image: To maximize the benefits of the latest features and security updates, customers are strongly recommended to use the most recent HDInsight image number. By doing so, organizations ensure that their HDInsight clusters are fortified against the latest threats and vulnerabilities. \n \n \n \n Accessing Fixed CVE Details: For users seeking detailed information about the fixed Common Vulnerabilities and Exposures (CVEs), the Ubuntu CVE site serves as a valuable resource. Here, users can access comprehensive insights into the specific vulnerabilities addressed in each release, empowering them to make informed decisions about their security posture. \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2996","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4361092":{"__typename":"Conversation","id":"conversation:4361092","topic":{"__typename":"BlogTopicMessage","uid":4361092},"lastPostingActivityTime":"2025-01-10T13:44:29.726-08:00","solved":false},"User:user:2701849":{"__typename":"User","uid":2701849,"login":"rabindori","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-9.svg?time=0"},"id":"user:2701849"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLTFndTVTdQ?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLTFndTVTdQ?revision=7","title":"Designer.jpeg","associationType":"COVER","width":1022,"height":420,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLTMxS01FdA?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLTMxS01FdA?revision=7","title":"clipboard_image-1-1735506546518.png","associationType":"BODY","width":1462,"height":818,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLUd3Z1FVTw?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLUd3Z1FVTw?revision=7","title":"clipboard_image-2-1735506842152.png","associationType":"BODY","width":1600,"height":952,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLTBDMVhhbQ?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLTBDMVhhbQ?revision=7","title":"clipboard_image-8-1735508479032.png","associationType":"BODY","width":1475,"height":763,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLU1uWHh0dw?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLU1uWHh0dw?revision=7","title":"clipboard_image-3-1735582665482.png","associationType":"BODY","width":1096,"height":792,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLVNDS3I2Qg?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLVNDS3I2Qg?revision=7","title":"clipboard_image-1-1735582569477.png","associationType":"BODY","width":1083,"height":777,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLWlCWkxVMw?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLWlCWkxVMw?revision=7","title":"clipboard_image-2-1735582595979.png","associationType":"BODY","width":1099,"height":783,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLUxRVURjRg?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLUxRVURjRg?revision=7","title":"clipboard_image-2-1735580547113.png","associationType":"BODY","width":969,"height":776,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLXRyNUlXSA?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLXRyNUlXSA?revision=7","title":"clipboard_image-4-1735582742412.png","associationType":"BODY","width":1593,"height":874,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLThxQkFEcQ?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzYxMDkyLThxQkFEcQ?revision=7","title":"clipboard_image-1-1735582793264.png","associationType":"BODY","width":1373,"height":782,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/g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Log Management with Microsoft Fabric","conversation":{"__ref":"Conversation:conversation:4361092"},"id":"message:4361092","revisionNum":7,"uid":4361092,"depth":0,"board":{"__ref":"Blog:board:AnalyticsonAzure"},"author":{"__ref":"User:user:2701849"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":1965},"postTime":"2025-01-10T13:44:29.726-08:00","lastPublishTime":"2025-01-10T13:44:29.726-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n In the era of digital transformation, managing and analyzing log files in real-time is essential for maintaining application health, security, and performance. There are many 3rd party solutions in this area allowing collecting / processing storing, analyzing and acting upon this data source. But sometimes as your systems scale, those solution can become very costly, their cost model increases based on the amount of ingested data and not according to the real resources utilization or customer value \n This blog post explores a robust architecture leveraging Microsoft Fabric SaaS platform focused on its Realtime Intelligence capabilities for efficient log files collection processing and analysis. \n The use cases can vary from simple application errors troubleshooting, to more advanced use case such as application trends detection: detecting slowly degrading performance issues: like average user session in the app for specific activities last more than expected to more proactive monitoring using log based KPIs definition and monitoring those APIS for alerts generation \n Regarding cost , since Fabric provides a complete separation between compute and storage you can grow your data without necessarily growing your compute costs and you still pay for the resources that re used in a pay as you go model. \n Architecture Overview \n The proposed architecture integrates Microsoft Fabric’s Real time intelligence (Realtime Hub) with your source log files to create a seamless, near real-time log collection solution \n It is based on Microsoft Fabric: a SAAS solution which is a unified suite integrating several best of breed Microsoft analytical experiences. Fabric is a modern data/ai platform based on unified and open data formats (parquet/delta) allowing both classical data lakes experiences using both traditional Lakehouse/warehouse SQL analytics as well as real-time intelligence on semi structured data , all in on a lake-centric SaaS platform. Fabric's open foundation with built-in governance enables you to connect to various clouds and tools while maintaining data trust. \n \n This is High level Overview of Realtime Intelligence inside Fabric \n \n \n Log events - Fabric based Architecture \n When looking in more details a solution for log collection processing storage and analysis we propose the following architecture \n \n Now let's discuss it in more details: \n General notes: \n Since Fabric is a SAAS solution, all the components can be used without deploying any infrastructure in advance, just by a click of a button and very simple configurations you can customize the relevant components for this solution \n The main components used in this solution are Data Pipeline Onelake and Eventhouse \n Our data source for this example is taken from this public git repo: \n https://github.com/logpai/loghub/tree/master/Spark \n The files were taken and stored inside an S3 bucket to simulate the easiness of the data pipeline integration to external data sources. \n A typical log file looks like this : \n 16/07/26 12:00:30 INFO util.Utils: Successfully started service 'sparkDriverActorSystem' on port 59219. \n16/07/26 12:00:30 INFO spark.SparkEnv: Registering MapOutputTracker \n16/07/26 12:00:30 INFO spark.SparkEnv: Registering BlockManagerMaster \n16/07/26 12:00:30 INFO storage.DiskBlockManager: Created local directory at /opt/hdfs/nodemanager/usercache/curi/appcache/application_1460011102909_0176/blockmgr-5ea750cb-dd00-4593-8b55-4fec98723714 16/07/26 12:00:30 INFO storage.MemoryStore: MemoryStore started with capacity 2.4 GB \n \n Components \n Data Pipeline \n \n First challenge to solve is how to bring the log files from your system into Fabric this is the Log collection phase: many solutions exist for this phase each with its pros and cons \n In Fabric the standard approach to bring data in is by use of Copy Activity in ADF or in its Fabric SAAS version is now called Data Pipeline: Data pipeline is a low code / no code tool allowing to manage and automate the process of moving and transforming data within Microsoft Fabric, a serverless ETL tool with more than 100 connectors enabling integration with a wide variety of data sources, including databases, cloud services, file systems, and more. In addition, it supports an on prem agent called self-hosted integration runtime, this agent that you install on a VM, acts as a bridge allowing to run your pipeline on a local VM and securing your connection from on prem network to the cloud \n Let’s describe in more details our solution data pipeline: \n \n Bear in mind ADF is very flexible and supports reading at scale from a wide range of data sources / files integrated as well to all major cloud vendors from blob storage retrieval : like S3, GCS, Oracle Cloud, File systems, FTP/SFTP etc so that even if your files are generated externally to Azure this is not an issue at all. \n \n Visualization of Fabric Data Pipeline \n \n \n Log Collection \n \n \n \n \n ADF Copy Activity: Inside Data pipeline we will create an Activity called Copy Activity with the following basic config\n \n Source: mapped to your data sources: it can be azure blob storage with container containing the log files, other cloud object storage like S3 or GCS , log files will be retrieved in general from a specific container/folder and are fetched based on some prefix/suffix in the file name. To support incremental load process we can configure it to delete the source files that it reads so that once the files are successfully transferred to their target they will be automatically deleted from their source . On the next iteration pipeline will not have to process the same files again. \n Sink: Onelake/Lakehouse folder: we create ahead of time a Lakehouse which is an abstract data container allowing to hold and manage at scale your data either structured or unstructured, we will then select it from the list of connectors (look for Onelake/Lakehouse) \n Log Shippers: This is an optional component, sometimes it is not allowed for the ETL to access your OnPrem Vnet , in this case tools like Fluentd , Filebeat , Open Telemetry collector used to forward your application collected logs to the main entry point of the system: the Azure Blob Storage. \n Azcopy CLI: if you don’t wish to invest into expensive tools and all you need to copy your data in a scale/secure manner to Azure Storage, you might consider create your own log shipping solution based on the free Azcopy tool together withs some basic scripting around it for scheduling: Azcopy is a command-line utility designed for high-performance uploading, downloading, and copying data to and from Microsoft Azure Blob and File storage. \n \n \n \n \n \n \n \n Fabric first Activity : Copy from Source Bucket to Lakehouse \n \n \n Log Preparation \n \n Upon log files landing in the azure blob storage, EventStream can be used to trigger the Data Pipeline that will handle the data preparation and loading phase. \n So what is Data preparation phase’s main purpose? \n After the log files land in the storage and before they are loaded to the realtime logs database the KQL Database , it might be necessary to transform the data with some basic manipulations . The reasons for that might be different \n A Few examples \n \n \n \n \n \n Bad data formats: for example, sometimes logs files contain problematic characters like new lines inside a row (stack trace error message with new lines as part of the message field of the record) \n Metadata enrichment: sometimes the log file names contain in their name some meaningful data : for example file name describes the originating process name / server name , so this metadata can be lost once the file content is loaded into database \n Regulation restrictions: sometimes logs contain private data like names, credit card numbers, social security number etc called PII that must be removed , hashed or encrypted before the load to database \n \n \n \n \n \n In our case we will be running a pyspark notebook who reads the files from Onelake folder, fixes the new lines inside a row issue, and create new files in \n another Onelake folder, we call this notebook with a base parameter called log_path that defines the log files location on the Onelake to read from \n \n \n Fabric second Activity : Running the Notebook \n \n \n Log Loading \n \n Inside Data pipeline , the last step, after the transformation phase, we call again the Copy data activity but this time source and sink are differen: \n \n \n \n Source: Lakehouse folder (previous notebook output) \n Sink: Evenhouse specific Table (created ahead of time): it is basically an empty table (lograw) \n \n \n \n \n Visualization of Fabric last Activity : Loading to EventHouse \n \n In summary for this stage the log collection and preparation: we broke this into 3 data pipeline activities: \n \n Copy Activity: Read the log files from source: This is the first step of the log ingestion pipeline it is running inside our orchestrator Data pipeline. \n Run Notebook Activity : Transform the log files : this is the execution of a single or chain of notebooks \n Copy Activity : Load the log files into destination datatbase : KQL inside Evenhouse : the logs database table called lograw, it is a specific table created ahead of time inside EventHouse Database \n \n Inside The Eventhouse \n We needed to create a KQL database with a table to hold the raw ingested log records \n KQL datbase is a scalable and efficient storage solution for log files, optimized for high-volume data ingestion and retrieval. Eventhouses and KQL databases operate on a fully managed Kusto engine. With an Eventhouse or KQL database, you can expect available compute for your analytics within 5 to 10 seconds. The compute resources grow with your data analytic needs. \n \n \n \n Log Ingestion to KQL Database with Update Policy \n \n \n We can separate the ETL transformation logic of what happens to the data before, it reaches the Eventhouse KQL database and after that. Before it reached the database , the only transformation we did was calling during the data pipeline a notebook to handle the new lines merge logic, This cannot be easily done as part of the database ingestion logic simply because when we try to load the files with new lines as part of a field of a record , it breaks the convention and what happens is that the ingestion process creates separate table records for each new line of the exceptions stacktrace. \n On the other hand, we might need to define basic transformation rules: such as date formatting, type conversion (string to numbers) , parse and extract some interesting value from a String based on regular exception, create JSON (dynamic type) of a hierarchical string (XML / JSON string etc) for all these transformations we can work with what is called an update policy we can define a simple ETL logic inside KQL database as explained here \n During this step we create from logsraw staging table a new table called logparsed , that will be our destination final table for the log queries. \n \n Those are the KQL Tables defined to hold the log files \n .create table logsraw ( timestamp:string , log_level:string, module:string, message:string) .create table logsparsed ( formattedDatetime:datetime , log_level:string, log_module:string, message:string) \n \n This is the update policy that automatically converts data from, the staging table logsraw to the destination table logparsed \n .create-or-alter function parse_lograw() { logsraw | project formattedtime = todatetime(strcat(\"20\", substring(timestamp, 0, 2), \"-\", substring(timestamp, 3, 2), \"-\", substring(timestamp, 6, 2), \"T\", substring(timestamp, 9, 8))), log_level, logmodule=module, message } .alter table logsparsed policy update @'[{ \"IsEnabled\": true, \"Source\": \"logsraw\", \"Query\": \"parse_lograw()\", \"IsTransactional\": true, \"PropagateIngestionProperties\": true}]' \n \n Since we don't need to retain the data in the staging table (lograw) we can define a retention policy of 0 TTL like this : \n .alter-merge table logsraw policy retention softdelete = 0sec recoverability = disabled \n \n \n \n Query Log files \n \n \n After data is ingested and transformed it lands in a basic logs table that is schematized : logparsed, in general we have some common fields that are mapped to their own columns like : log level (INFO/ ERROR/ DEBUG) , log category , log timestamp (a datetime typed column) and log message which can be in general either a simple error string or a complex JSON formatted string in which case it is usually preferred to be converted to dynamic type that will bring additional benefits like simplified query logic, and reduced data processing (to avoid expensive joins) \n \n Example for Typical Log Queries \n \n \n \n \n Category \n Purpose \n KQL Query \n \n \n Troubleshooting \n Looking for an error at specific datetime range \n \n logsparsed | where message contains \"Exception\" and formattedDatetime between ( datetime(2016-07-26T12:10:00) .. datetime(2016-07-26T12:20:00)) \n \n \n \n \n Statistics \n \n \n Basic statistics \n Min/Max timestamp of log events \n \n \n logsparsed | summarize minTimestamp=min(formattedDatetime), maxTimestamp=max(formattedDatetime) \n \n \n \n \n Exceptions Stats \n \n \n Check Exceptions Distributions \n \n \n logsparsed | extend exceptionType = case(message contains \"java.io.IOException\",\"IOException\", message contains \"java.lang.IllegalStateException\",\"IllegalStateException\", message contains \"org.apache.spark.rpc.RpcTimeoutException\", \"RpcTimeoutException\", message contains \"org.apache.spark.SparkException\",\"SparkException\", message contains \"Exception\",\"Other Exceptions\", \"No Exception\") | where exceptionType != \"No Exception\" | summarize count() by exceptionType \n \n \n \n \n Log Module Stats \n \n \n Check Modules Distribution \n \n \n logsparsed | summarize count() by log_module | order by count_ desc | take 10 \n \n \n \n \n \n \n \n \n Realtime Dashboards \n After querying the logs, it is possible to visualize the query results in Realtime dashboards, for that all what’s required \n \n \n \n Select the query \n Click on Pin to Dashboard \n \n \n \n \n \n After adding the queries to tiles inside the dashboard this is a typical dashboard we can easily build: \n \n Realtime dashboards can be configured to be refreshed in Realtime like illustrated here: in which case user can very easily configure how often to refresh the queries and visualization : at the extreme case it can be as low as Continuus \n \n \n \n There are many more capabilities implemented in the Real-Time Dashboard, like data exploration Alerting using Data Activator , conditional formatting (change items colors based on KPIs threshold) and this framework and capabilities are heavily invested and keep growing. \n \n What about AI Integration ? \n Machine Learning Models: Kusto supports out of the box time series analysis allowing for example anomaly detection: https://learn.microsoft.com/en-us/fabric/real-time-intelligence/dashboard-explore-data and clustering but if it’s not enough for you, you can always mirror the data of your KQL tables into Onelake delta parquet format by selecting OneLake availability \n This configuration will create another copy of your data in open format delta parquet : you have it available for any Spark/Python/SparkML/SQL analytics for whatever machine learning exploration and ML modeling you wish to explore train and serve \n This is illustrated here : \n \n Bear in mind , there is no additional storage cost to turn on OneLake availability \n Conclusion \n A well-designed real-time intelligence solution for log file management using Microsoft Fabric and EventHouse can significantly enhance an organization’s ability to monitor, analyze, and respond to log events. By leveraging modern technologies and best practices, organizations can gain valuable insights and maintain robust system performance and security. 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