External Data Sharing With Microsoft Fabric
The demands and growth of data for external analytics consumption is rapidly growing. There are many options to share data externally and the field is very dynamic. One of the most frictionless and easy onboarding steps for external data sharing we will explore is with Microsoft Fabric. This external data allows users to share data from their tenant with users in another Microsoft Fabric tenant.Advanced Time Series Anomaly Detector in Fabric
Anomaly Detector, one of Azure AI services, enables you to monitor and detect anomalies in your time series data. This service is being retired by October 2026, and as part of the migration process the anomaly detection algorithms were open sourced and published by a new Python package and weoffer a time series anomaly detection workflow in Microsoft Fabric data platform.
Using Spark to track PowerBI Activity Events
Have you ever wondered what goes on behind the scenes when users interact with your PowerBI reports? The PowerBI REST API opens a door to a wealth of activity data that can be harnessed to gain insights into user behavior and system performance. In this blog post, we'll explore the exciting possibilities of using Apache Spark to tap into the PowerBI REST API, enabling you to track and analyze activity events seamlessly. By the end, you'll be equipped to create a comprehensive PowerBI dashboard that gives you a real-time pulse on your PowerBI environment. Discover how this integration not only empowers you to monitor user interactions but also opens avenues for optimizing report performance, ensuring data security, and making informed decisions for your PowerBI deployment. This will guide you through the process of connecting Spark to the PowerBI REST API, retrieving detailed activity logs, and transforming the data into actionable insights. Buckle up for a journey into the world of real-time analytics and data-driven decision-making. Ready to supercharge your PowerBI monitoring? Let's dive in!Migration of Apache Spark from HDInsight 5.0 to HDInsight 5.1
Azure HDInsight Spark 5.0 to HDI 5.1 Migration A new version of HDInsight 5.1 is released with Spark 3.3.1. This release improves join query performance via Bloom filters, increases the Pandas API coverage with the support of popular Pandas features such as datetime.timedelta and merge_asof, simplifies the migration from traditional data warehouses by improving ANSI compliance and supporting dozens of new built-in functions. In this article we will discuss about the migration of user applications from HDInsight Spark 3.1 to HDInsight Spark 3.3
Data Vault 2.0 using Databricks Lakehouse Architecture on Azure
This Article is about Data Vault 2.0 using Databricks Lakehouse Architecture on Azure and is presented in partnership with VaultSpeed and Scalefree our Microsoft Partner Network on Data Warehouse Automation and is part of Blog series. Please see the Landing Page here for more Articles. This Article isAuthored By Jonas De Keuster& Koen Moermansfrom VaultSpeedandCo-authored with Michael Olschimke, co-founder and CEO at Scalefree International GmbH TheTechnical Review is done by Ian Clarke, Naveed Hussain, Irfan Maroofand Hajar Habjaoui– GBBs (Cloud Scale Analytics) for EMEA at Microsoft
