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Microsoft Fabric Updates Blog

  1. Updates
  2. Santhosh Kumar Ravindran

Microsoft Fabric Spark: Native Execution Engine now generally available

The Fabric Spark Native Execution Engine (NEE) is now generally available (GA) as part of Fabric Runtime 1.3. This C++-based vectorized engine (built on Apache Gluten and Velox) runs Spark workloads directly on the lakehouse, requiring no code changes or new libraries. It supports Spark 3.5 APIs and both Parquet and Delta Lake formats, so … Continue reading “Microsoft Fabric Spark: Native Execution Engine now generally available”

High Concurrency mode for notebooks in pipelines (Generally Available)

High Concurrency mode for notebooks in pipelines is now generally available (GA)! This powerful feature enhances enterprise data ingestion and transformation by optimizing session sharing within one of the most widely used orchestration mechanisms. With this release, we’re also introducing Comprehensive Monitoring for High-Concurrency Spark Applications, bringing deeper visibility and control to your workloads. Key … Continue reading “High Concurrency mode for notebooks in pipelines (Generally Available)”

Supercharge your workloads: write-optimized default Spark configurations in Microsoft Fabric

Introducing predefined Spark resource profiles in Microsoft Fabric—making it easier than ever for data engineers to optimize their compute configurations based on workload needs. Whether you’re handling read-heavy, write-heavy, or mixed workloads, Fabric now provides a property bag-based approach that streamlines Spark tuning with just a simple setting. With these new configurations, users can effortlessly … Continue reading “Supercharge your workloads: write-optimized default Spark configurations in Microsoft Fabric”

Introducing Autoscale Billing for Spark in Microsoft Fabric

We are introducing Autoscale Billing for Spark in Microsoft Fabric, a new billing model designed to offer greater flexibility and cost efficiency for Spark workloads. With this model, when enabled, your Spark workloads will no longer directly consume the Fabric capacity this billing option is enabled on; instead, they will run alongside your existing capacity (F2 … Continue reading “Introducing Autoscale Billing for Spark in Microsoft Fabric”

Optimizing Spark Compute for Medallion Architectures in Microsoft Fabric

Guidance to Maximizing Productivity and Efficiency for your Data Engineering Workloads Data engineering teams often grapple with the complexities of planning and configuring compute resources for their data platforms. This is especially true when working with large-scale, complex datasets and demanding downstream SLAs. A one-size-fits-all approach is rarely effective, as different data layers and datasets … Continue reading “Optimizing Spark Compute for Medallion Architectures in Microsoft Fabric”