Decision Guide for Selecting an Analytical Data Store in Microsoft Fabric
Learn how to select an analytical data store in Microsoft Fabric based on your workload's data volumes, data type requirements, compute engine preferences, data ingestion patterns, data transformation needs, query patterns, and other factors.Approaches to Integrating Azure Databricks with Microsoft Fabric: The Better Together Story!
Azure Databricks and Microsoft Fabric can be combined to create a unified and scalable analytics ecosystem. This document outlines eight distinct integration approaches, each accompanied by step-by-step implementation guidance and key design considerations. These methods are not prescriptive—your cloud architecture team can choose the integration strategy that best aligns with your organization’s governance model, workload requirements and platform preferences. Whether you prioritize centralized orchestration, direct data access, or seamless reporting, the flexibility of these options allows you to tailor the solution to your specific needs.Microsoft Fabric for those who know nothing about Fabric
This is not any regular blog, don't click on this blog if you don't want to get convinced, if you are curious, click and see. You will end up falling in love with Microsoft Fabric. Yes, that's because you will love it when you get to know what it is.
Elevating care management analytics with Copilot for Power BI
Healthcare data solutions care management analytics capability offers a comprehensive template using the medallion Lakehouse architecture to unify and analyze diverse data sets of meaningful insights. This enables enhanced care coordination, improved patient outcomes, and scalable, sustainable insights. As the healthcare industry faces rising costs and growing demand for personalized care, data and AI are becoming critical tools. Copilot for Power BI leads this shift, blending AI-driven insights with advanced visualization to revolutionize care delivery. What is Copilot for Power BI? Copilot is an AI-powered assistant embedded directly into Power BI, Microsoft's interactive data visualization platform. By leveraging natural language processing and machine learning, Copilot helps users interact with their data more intuitively whether by asking questions in plain English, generating complex calculations, or uncovering patterns that might otherwise go unnoticed. Copilot for Power BI is embedded within healthcare data solutions, allowing care management—one of its core capabilities—to harness these AI-driven insights. In the context of care management analytics, this means turning a sea of clinical, claims, and operational data into actionable insights without needing to write a single line of code. This empowers teams across all technical levels to gain value from data. Driving better outcomes through intelligent insights in care management analytics The Care Management Analytics solution, built on the Healthcare data solutions platform, leverages Power BI with Copilot embedded directly within it. Here’s how Copilot for Power BI is revolutionizing care management: Enhancing decision-making with AI Traditionally, deriving insights from healthcare data required technical expertise and hours of analysis. Copilot simplifies this by allowing care managers and clinicians to ask questions like “Analyze which medical conditions have the highest cost and prevalence in low-income regions.” The AI interprets these queries and responds with visualizations, trends, and predictions—empowering faster, data-driven decisions. Proactive care planning By analyzing historical and real-time data, Copilot helps identify at-risk patients before complications arise. This enables care teams to intervene earlier, design more personalized care plans, and ultimately improve outcomes while reducing unnecessary hospitalizations. Operational efficiency From staffing models to resource allocation, Copilot provides visibility into operational metrics that can drive significant efficiency gains. Healthcare leaders can quickly identify bottlenecks, monitor key performance indicators (KPIs) and simulate “what-if” scenarios, enabling more i nformed, data-backed decisions on care delivery models. Reducing costs without compromising quality Cost containment is a constant challenge in healthcare. By highlighting areas of high spend and correlating them with clinical outcomes, Copilot empowers organizations to optimize care pathways and eliminate inefficiencies ensuring patients receive the right care at the right time, without waste. Democratizing data access Perhaps one of the most transformative aspects of Copilot is how it democratizes access to analytics. Non-technical users from care coordinators to nurse managers can interact with dashboards, explore data, and generate insights independently. This cultural shift encourages a more data-literate workforce and fosters collaboration across teams. Real-world impact Consider a healthcare system leveraging Power BI and Copilot to manage chronic disease populations more effectively. By combining claims data, social determinants of health (SDoH) indicators, and patient-reported outcomes, care teams can gain a comprehensive view of patient needs- enabling more coordinated care and proactively identifying care gaps. With these insights, organizations can launch targeted outreach initiatives that reduce avoidable emergency department (ED) visits, improve medication adherence, and ultimately enhance outcomes. The future is here The integration of Copilot for Power BI marks a pivotal moment for healthcare analytics. It bridges the gap between data and action, bringing AI to the frontlines of care. As the industry continues to embrace value-based care models, tools like Copilot will be essential in achieving the triple aim: better care, lower costs, and improved patient experience. Copilot is more than a tool — it is a strategic partner in you care transformation journey. Deployment of care management analytics Showcasing how a Population Health Director uncovers actionable insights through Copilot Note: To fully leverage the capabilities of the solution, please follow the deployment steps provided and use the sample data included with the Healthcare Data Solution. For more information on care management analytics, please review our detailed documentation and get started with transforming your healthcare data landscape today Overview of care management analytics - Microsoft Cloud for Healthcare | Microsoft Learn Deploy and analyze using Care management analytics - Training | Microsoft Learn. Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.
Upgrade performance, availability and security with new features in Azure Database for PostgreSQL
At Microsoft Build 2025 the Postgres on Azure team is announcing an exciting set of improvements and features for Azure Database for PostgreSQL. One area we are always focused on is the enterprise. This week we are delighted to announce improvements across the enterprise pillars of Performance, Availability and Security. In addition, we're improving Integration of Postgres workloads with services like ADF and Fabric. Here's a quick tour of the enterprise enhancements to Azure Database for PostgreSQL being announced this week. Performance and scale SSD v2 with HA support - Public Preview The public preview of zone-redundant high availability (HA) support for the Premium SSD v2 storage tier with Azure Database for PostgreSQL flexible server is now available. You can now enable High Availability with zone redundancy using Azure Premium SSD v2 when deploying flexible server, helping you achieve a Recovery Point Objective (RPO) of zero for mission-critical workloads. Premium SSD v2 offers sub-millisecond latency and outstanding performance at a low cost, making it ideal for IO-intensive, enterprise-grade workloads. With this update, you can significantly boost the price-performance of your PostgreSQL deployments on Azure and improve availability with reduced downtime during HA failover. The key benefits of SSD v2 include: Flexible disk sizing from 1 GiB to 64 TiB, with 1-GiB increment support Independent performance configuration: scale up to 80,000 IOPS and 1,200 MBps throughput without needing to provision larger disks To learn more about how to upgrade and best practices, visit: Premium SSDv2 PostgreSQL 17 Major Version Upgrade – Public Preview PostgreSQL version 17 brings a host of performance improvements, including a more efficient VACUUM process, faster sequential scans via streaming IO, and optimized query execution. Now, with the public preview of in-place major version upgrades to PostgreSQL 17 there is an easier path to v17 for your existing flexible server workloads. With this release, you can upgrade from earlier versions (14, 15, or 16) to PostgreSQL 17 without the need to migrate data or change server endpoints, simplifying the upgrade process and minimizing downtime. Azure’s in-place upgrade capability offers a native, low-disruption upgrade path directly from the Azure Portal or CLI. For upgrade steps and best practices, check out our detailed blog post. Availability Long-Term Backup (LTR) for Azure Database for PostgreSQL flexible server - Generally Available Long-term backups are essential for organizations with regulatory, compliance, and audit-driven requirements, especially in industries like finance and healthcare. Certifications such as HIPAA often mandate data retention periods up to 10 years, far exceeding the default 35-day retention limit provided by point-in-time restore (PITR) capabilities. Long-term backup for Azure Database for PostgreSQL flexible server, powered by Azure Backup is now generally available. With this release, you can now benefit from: Policy-driven, one-click enablement of long-term backups Resilient data retention across Azure Storage tiers Consumption-based pricing with no egress charges Support for restoring backups well beyond community-supported PostgreSQL versions This LTR capability uses a logical backup approach based on pg_dump and pg_restore, offering a flexible, open-source format that enhances portability and ensures your data can be restored across a variety of environments including Azure VMs, on-premises, or even other cloud providers. Learn more about long term retention: Backup and restore - Azure Database for PostgreSQL flexible server Azure Databases for PostgreSQL flexible server Resiliency Solution accelerator When it comes to ensuring business continuity, your database infrastructure is the most critical component. In addition to product documentation, it is important to have access to opinionated solution architecture, industry-proven recommended practices, and deployable infra-as-code that you can learn and customize to ensure an automated production-ready resilient infrastructure for your data. The Azure Database for PostgreSQL Resiliency Solution Accelerator is now available, providing a set of deployable architectures to ensure business continuity, minimize downtime, and protect data integrity during planned and unplanned events. In additional to architecture and recommended practices, a customizable Terraform deployment workflow is provided. Learn more: Azure Database for PostgreSQL Resiliency Solution Accelerator Security Automatic Customer Managed Key (CMK) version updates - Generally Available Azure Database for PostgreSQL flexible server data is fully encrypted, supporting both Service Managed and Customer Managed encryption keys (CMK). Automatic version updates for CMK (also known as “versionless keys”) is now generally available. This change simplifies the key lifecycle management by allowing PostgreSQL to automatically adopt new keys without needing manual updates. Combined with Azure Key Vault's auto-rotation feature this significantly reduces the management overhead of encryption key maintenance. Learn more about automatic CMK version updates. Azure confidential computing SKUs for flexible server - Public Preview Azure confidential computing enables secure sensitive and regulated data, preventing unwanted access of data in-use, by cloud providers, administrators, or external users. With the public preview of Azure confidential SKUs for Azure Database for PostgreSQL flexible server you can now select from a range of Confidential Computing VM sizes to run your PostgreSQL workloads in a hardware-based trusted execution environment (TEE). Azure confidential computing encrypts data in TEE, processing data in a verified environment, enabling you to securely process workloads while meeting compliance and regulatory demands. Learn more about confidential computing with the Azure Database for flexible server. Integration Entra Authentication for Azure Data Factory & Azure Synapse - Generally Available In an era of bring-your-own-device and cloud-enabled apps it is increasingly important for enterprises to maintain central control an identity-based security perimeter. With integrated Entra ID support, Azure Database for PostgreSQL flexible server allows you to bring your database workloads within this perimeter. But how do you securely connect to other services? Entra ID authentication is now supported in the Azure Data Factory and Azure Synapse connectors for Azure Database for PostgreSQL. This feature enables seamless, secure connectivity using Service Principal (key or certificate) and both User-Assigned and System-Assigned Managed Identities, streamlining access to your data pipelines and analytics workloads. Learn more about How to Connect from Azure Data Factory and Synapse Analytics to Azure Database for PostgreSQL. Fabric Data Factory – Upsert Method & Script Activity - Generally Available The Microsoft Fabric has become to go-to data analytics platform with services and tools for every data lifecycle state. To improve customization and fine-grained control over processing of PostgreSQL data, the Upsert Method and custom Script Activity are now generally available in Fabric Data Factory when using Azure Database for PostgreSQL as a source or sink. Upsert Method enables intelligent insert-or-update logic for PostgreSQL, making it easier to handle incremental data loads and change data capture (CDC) scenarios without complex workarounds. Script Activity allows you to embed and execute your own SQL scripts directly within pipelines—ideal for advanced transformations, procedural logic, and fine-grained control over data operations. These capabilities offer enhanced flexibility for building robust, enterprise-grade data workflows, simplifying your ETL processes. Connect to VS Code from the Azure Portal - Public Preview With the exciting announcement of a revamped VS Code PostgreSQL extension preview this week, we're adding a new connection option to the Azure Portal to connect to your flexible server with VS Code, creating a more unified and efficient developer experience. Here's why it matters: One Click Connectivity: No manual connection strings or configuration needed. Faster Onboarding: Go from provisioning a database in Azure to exploring and managing it in VS Code within seconds. Integrated Workflow: Manage infrastructure and development from a single, cohesive environment. Productivity: Connect directly from the Portal to leverage VS Code extension features like query editing, result views, and schema browsing. Where to learn more The Build 2025 announcements this week are just the latest in a compelling set of features delivered by the Azure Database for PostgreSQL team and build on our latest set of monthly feature updates (see: April 2025 Recap: Azure Database for PostgreSQL Flexible Server). Follow the Azure Database for PostgreSQL Blog where you'll see many of the latest updates from Build, including What's New with PostgreSQL @Build, and New Generative AI Features in Azure Database for PostgreSQL.Building Healthcare Research Data Platform using Microsoft Fabric
Co-Authors: Manoj Kumar, Mustafa Al-Durra PhD, Kemal Kepenek, Matt Dearing, Praneeth Sanapathi, Naveen Valluri Overview Research data platforms in healthcare providers, academic medical centers (AMCs), and research institutes support research, clinical decision making, and innovation. They consolidate data from various sources, making it accessible for comprehensive analysis and fostering collaboration among research teams. These platforms automate data collection, processing, and delivery, reducing time and effort needed for data management. This allows researchers to focus on their core activities while ensuring data security and regulatory compliance. The ability to work with multimodal data encourages interdisciplinary and interorganizational collaboration, uniting experts to address complex healthcare challenges. Current challenges Researchers face many common challenges as they work with multimodal healthcare data: Data integration and curation: The process of integrating various data types, such as clinical notes, imaging data, genomic information, and sensor data, presents significant challenges due to differences in formats, standards, and sources. Each AMC employs unique methods for data curation, with some utilizing on-premises solutions and others adopting hybrid cloud systems. No standardized approach currently exists for data curation, necessitating considerable organizational efforts to ensure data consistency and quality. Furthermore, data deidentification is often required to safeguard patient privacy. Data discovery and building cohorts: The lack of a unified multimodal data platform leads to the segregation of data across different modalities. Cohort discovery for each modality is performed separately and often lacks a self-service option, necessitating additional human resources to assist researchers in the data discovery process. This issue is particularly significant because researchers who require Institutional Review Board (IRB) approval cannot access the data beforehand but still need an effective method to identify and explore cohorts. Data delivery: Sensitive patient data, after institutional review board approval, must comply with privacy regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR), requiring secure transfer to prevent breaches. The data, sourced from various systems, needs processing for research readiness. Delivering unified data from modalities like imaging, genomics, and health records is challenging. Typically, research IT teams curate cohort data and deliver it to an SQL database or a file share, accessed by researchers via secure virtual machines. This method often leads to data duplication, creating significant overhead due to numerous ongoing research projects. Cost management: Research projects are funded by government grants and private organizations. Managing these costs is challenging. Research IT departments often implement chargebacks for transparency and accountability in resource use. However, there is a disconnect between funding models and operations. Research teams favor capital expenditure (CapEx) with upfront funding for long-term resources, while cloud platforms operate on operational expenditure (OpEx), incurring ongoing costs based on usage. This shift can lead to concerns about unpredictable costs and budgeting difficulties. Bridging this gap requires careful planning, communication, and hybrid financial strategies to align research needs with cloud-based systems. Compliance with regulations: Healthcare research uses sensitive patient data, requiring strict adherence to HIPAA and GDPR. Transparency in data handling is essential but complex. Researchers must document disclosures thoroughly, detailing who accessed the data and for what purpose. However, tracking and auditing are often fragmented due to inconsistent systems. Variability in disclosure requirements from different agencies adds to compliance challenges. Balancing an auditable trail with privacy and manageable administrative tasks is crucial. Research data platform requirements Ability to curate multi modal data into the research data platform Ability for researchers to identify cohorts (without seeing data) to submit data requests to IRB Automated data delivery after IRB workflow approves the request to access relevant data Tools for researchers as part of the same platform Secure and regulatory-compliant environment for research. An approach to building a research data platform using Microsoft Fabric This article serves as a guide to healthcare organizations, offering a point of view and a prescriptive guidance on building a research data platform using Microsoft Fabric. The solution uses several features from healthcare data solutions in Microsoft Fabric, including its discover and build cohorts capability, and features from the Fabric platform. Microsoft Fabric: is a unified, AI-powered data platform designed to simplify data management and analytics. It integrates various tools and services to handle every stage of the data lifecycle, including ingestion, preparation, storage, analysis, and visualization. Fabric is built on a Software as a Service (SaaS) foundation, offering seamless experience for organizations to make data-driven decisions. For additional details, refer to the following link: What is Microsoft Fabric - Microsoft Fabric | Microsoft Learn Healthcare data solutions in Fabric: Healthcare data solutions in Fabric help you accelerate time to value by addressing the critical need to efficiently transform healthcare data into a suitable format for analysis. With these solutions, you can conduct exploratory analysis, run large-scale analytics, and power generative AI with your healthcare data. By using intuitive tools such as data pipelines and transformations, you can easily navigate and process complex datasets, overcoming the inherent challenges associated with unstructured data formats. For additional details, refer to the following links: Healthcare data solutions in Microsoft Fabric - Microsoft Cloud for Healthcare | Microsoft Learn Discover and build cohorts: Discover and build cohorts (preview) capability in healthcare data solutions enables healthcare organizations to efficiently analyze and query healthcare data from multiple sources and formats. It simplifies the preparation of data for health trend studies, clinical trials, quality assessments, historical research, and AI development. It supports natural language queries for multimodal data exploration and cohort building, making it ideal for research and AI-driven projects. For additional details, refer to the following link: Overview of discover and build cohorts (preview) - Microsoft Cloud for Healthcare | Microsoft Learn The proposal for research data platform architecture builds upon the following foundational premises: Recognition of Fabric as the all-in-one data storage, processing, management and analytics platform with enterprise-level features around security, availability and self-service. Adoption of Fabric Workspace(s) as the security boundary (a secure logical container) for maintaining data platform items (data storage and processing assets). Fabric workspaces may be provisioned for and used by different research data platform stakeholders (groups of users) with different requirements around use cases, data privacy, data sensitivity and access security. Use of healthcare data solutions in Fabric, as the core capability to maintain healthcare data assets in a standard (interoperable) manner. Healthcare data solutions enables the storage and processing of several healthcare data modalities and formats that follow industry standards (for example, clinical modality in FHIR® NDJSON format and Clinical-Imaging modality’s DICOM® format). Industry standards make it easier for research data platform stakeholders to share (exchange) data and insights within their own organization as well as (when needed) with other organizations that they collaborate with. Use of Fabric native capabilities to address requirements that may not (yet) have been implemented for healthcare specific needs. This provides the research data platform stakeholders with the flexibility to develop various data storage and processing workloads easily in a low (or no) code manner. Fig – Conceptual architecture of research data platform in Microsoft Fabric Note: This diagram is an architectural pattern and does not constitute one to one mapping of existing Microsoft products. Organizing source data in data workspace (One Data Hub in the above diagram) Organize your enterprise data into a data workspace that could be leveraged for research purposes. This acts as a ‘One Data Hub’ for the research data platform. Multiple Lakehouse can be present in this workspace. There should be at least one Lakehouse that organizes data using ‘unified folder structure’ best practice. Convert data from non-supported format to healthcare data solutions supported format to leverage out of the box transformation for multimodal data: For healthcare data solutions supported modalities: Implement custom transformations to convert data to supported modalities/format. For unsupported modalities: Implement extensions to bronze Lakehouse to accommodate additional data modalities. Epic data availability: Epic supports FHIR data export using Bulk FHIR APIs. If your dataset meets the use cases of Epic Bulk Data, you can store the resulting FHIR resources into One Data Hub for further transformation. Avoid data content duplication: Data duplication cannot be totally avoided. However, the same file and same content are never duplicated. There will be situations when data needs to be transformed to suit the needs of existing transformation pipelines for accelerating research data platform development. Additionally, OneLake in Fabric storage, where Lakehouse is maintained, uses file compression. Healthcare data solutions in Fabric has functionality to compress raw files to zip and always writes structured data to delta parquet which is a higher compressed format. More information can be found here - Data architecture and management in healthcare data solutions - Microsoft Cloud for Healthcare | Microsoft Learn Curating data for research (One Analytics workspace in the above diagram) Implement and extend Silver Lakehouse: A flattened FHIR® data model is provided by healthcare data solutions out of the box within the Silver Lakehouse. Extending the existing data model is possible through adding new columns to existing tables or through adding new tables in the Silver Lakehouse. If there is a need to introduce a different data model altogether, it is best to implement it using a different Lakehouse. Implement and extend Gold Lakehouse: Deploy and extend Observational Medical Outcomes Partnership Common Data Model (OMOP CDM): Deploy OMOP CDM 5.4 out of the box with healthcare data solutions deployment. Extend OMOP CDM to accommodate additional modalities. For example, implement Gene sequencing, Variant occurrence and Variant annotation tables to add genomics modality into OMOP CDM or implement medical imaging data on OMOP CDM as described here - Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension - PubMed Implement custom Gold Lakehouse(s): Implement other custom Gold Lakehouse using Fabric tools that run your transformation logic from Silver to Gold. These Lakehouse cannot be connected to discover and build cohorts capability within healthcare data solutions. Customers that need access to custom data can connect their custom cohort browsers to the SQL Analytics Endpoint(s) of their custom Gold Lakehouse(s). Enable data de-identification: Microsoft provides several solutions that can be used to implement a comprehensive de-identification solution that customers expect. Refer to the articles below for details. Dynamic data masking in Fabric Data Warehouse - Microsoft Fabric | Microsoft Learn Row-level security in Fabric data warehousing - Microsoft Fabric | Microsoft Learn Column-level security in Fabric data warehousing - Microsoft Fabric | Microsoft Learn Announcing a de-identification service for Health and Life Sciences | Microsoft Community Hub Cohort discovery using cohort builder tool Microsoft’s cohort browser: Today Discovery and Build Cohort supports eyes-on cohort discovery. This is an out of the box solution that is part of healthcare data solutions in Fabric. When eyes off discovery is supported, researchers as well as research IT can benefit from both eyes off and eyes on discovery and cohort building. 3rd-party cohort browser (e.g., OHDSI Atlas): Most 3rd party cohort browsers (E.g. OHDSI Atlas) and home-grown cohort browsers typically support connection to a SQL endpoint. Microsoft Fabric platform provides the capability of exposing SQL endpoint from a Lakehouse that can be connected to a 3rd party cohort browser to perform cohort discovery. Automated data delivery Creating research workspaces with cohort needed for research: Create separate workspaces for different research projects to keep Fabric items distinct and project specific using Fabric APIs. Assign workspaces to a Fabric capacity: Note: When needed, and if the organization has more than one Fabric capacity provisioned, workspace assignment can be spread across different capacities to help manage cost and performance. Next, set up a Lakehouse and provide access for team members (as per IRB approval list). This ensures both access and security at the workspace level. Export data to research workspace (format desired by researchers): Currently, DBC exports data as CSV/JSON files stored in a Lakehouse within the same workspace. Shortcut the destination Lakehouse into research workspace to keep the sanity of cohort data. Tools for researchers: Fabric provides several data engineering and data science tools out of the box that researchers can leverage to perform research. The following are some of the documents that customers can use to enable researchers with the tools of choice. Data science in Microsoft Fabric - Microsoft Fabric | Microsoft Learn Create, configure, and use an environment in Fabric - Microsoft Fabric | Microsoft Learn Migrate libraries and properties to a default environment - Microsoft Fabric | Microsoft Learn Charge back: Fabric compute pricing depends on the chosen Fabric capacity SKU. Assigning different Fabric capacities to different projects or groups within the same cost center can facilitate chargeback. See the step mentioned above on assigning a workspace to a Fabric capacity during workspace creation. Manage historic data migration to the research data platform on Fabric In most instances, customers already possess a research data platform. They seek to transition to this proposed solution without disrupting their current research data flow and obligations. Follow this approach to migrate or use data from the existing platform to the new one: Use your current research data platform as a Lakehouse or a Data Warehouse in Fabric (take advantage of Shortcut and Mirroring features available in Fabric). Fabric offers cross-database query, i.e. allowing to query and join multiple Lakehouse and data warehouses in a single query. Customers can choose how and where to implement such queries to augment the healthcare data solutions datasets with their existing datasets, all natively in Fabric. A bridge/mapping layer can be built to link the old and the new in a cross-relational way. Conceptually, such an approach has also ties to Bring Your Own Database (BYO-DB) requirement, which is the ability to bring custom defined format and still be able to easily convert to healthcare data solutions specific format. Other workflow integration Integrate research data platform with IRB workflow: IRB workflows are dependent on the tools utilized. For instance, eIRB solution from Huron. While there is currently no direct integration between IRB workflows and the research data platform on Fabric, it is possible to develop a connector using Power Platform integration with Fabric. Specific details are not available at this time as this remains an exploratory initiative. Another approach will be to use Fabric REST APIs (as a pro-code method) that can enable richer integration between Fabric and the 3 rd -party system, and a better consuming user experience at the end. Capture logs necessary for “accounting of disclosures”: Logs in Fabric can be captured at event level. It’s up to the customer to decide the level and type of logs that need to be captured for accounting of disclosure. This will need some custom implementation. One such capability of Fabric that can be used is: Track user activities in Microsoft Fabric - Microsoft Fabric | Microsoft Learn FHIR® is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office and is used with their permission. DICOM® is the registered trademark of the National Electrical Manufacturers Association (NEMA) for its Standards publications relating to digital communications of medical information. If you are a Microsoft customer needing further information, support, or guidance related to the content in this blog, we recommend you reach out to your Microsoft account team in order to set up a discussion with the authors.Microsoft Fabric & AI Learning Hackathon Informational AMA: What will you build?
The Microsoft Fabric & AI Learning Hackathon is underway and we're excited to learn about your project ideas and questions! Join us for an interactive session where we'll look at recent announcements from the European Microsoft Fabric Community Conference, share tips and best practices for competing in the Hackathon, provide technical and informational support, and connect you with expert resources to support ongoing development of your Hackathon project! An AMA is a live text-based online event similar to an “Ask Me Anything” on Reddit. This AMA gives you the opportunity to connect with Microsoft product experts who will be on hand to answer your questions and listen to feedback. The AMA takes place entirely in the comments below. There is no additional video or audio link as this is text-based. Check out this blog post for the official announcement about the Hackathon and check out the Fabric Hackathon website for all the details about the Hackathon. Feel free to post your questions anytime in the comments below beforehand, if it fits your schedule or time zone better, though questions will not be answered until the live hour.What’s New in Kusto – Build 2023 !
We are excited to share the latest features and improvements in Kusto that promise to make your data analysis experience more seamless and productive than ever before. We have been busy working with our customers over the last few months to bring exciting new GA & Preview features to Build 2023, with a raft of improvements and innovations.
