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Microsoft Fabric Updates Blog

  1. Aktualisierungen
  2. Santhosh Kumar Ravindran

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”

Introducing High Concurrency Mode for Notebooks in Pipelines for Fabric Spark

We’re excited to introduce high concurrency mode for notebooks in pipelines, bringing session sharing to one of the most popular orchestration mechanisms for enterprise data ingestion and transformation. Notebooks will now automatically be packed into an active high concurrency session without compromising performance or security, while paying for a single session. Key Benefits: Why Use … Continue reading “Introducing High Concurrency Mode for Notebooks in Pipelines for Fabric Spark”

Introducing Capacity Pools for Data Engineering and Data Science in Microsoft Fabric

We are excited to announce the Capacity Pools for Data Engineering and Data Science in Microsoft Fabric. As part of the Data Engineering and Science settings in the Admin portal, capacity administrators can create custom pools based on their workload requirements. Optimizing Cloud Spend and Managing Compute Resources In enterprise environments, managing cloud spending and … Continue reading “Introducing Capacity Pools for Data Engineering and Data Science in Microsoft Fabric”

Introducing Optimistic Job Admission for Fabric Spark

We are excited to announce a new feature which has been a long ask from Synapse Spark customers, Optimistic Job Admission for Spark in Microsoft Fabric.This feature brings in more flexibility to optimize for concurrency usage (in some cases ~12X increase) and prevents job starvation. This job admission approach aims to reduce the frequency of … Continue reading “Introducing Optimistic Job Admission for Fabric Spark”

Introducing Job Queueing for Notebook in Microsoft Fabric

Users orchestrate their data engineering or data science processes using notebooks and in most of the enterprise scenarios pipelines and job schedulers are used as a primary option to schedule and trigger these Spark jobs. We are thrilled to announce a new feature Job Queueing for Notebook Jobs in Microsoft Fabric. This feature aims to … Continue reading “Introducing Job Queueing for Notebook in Microsoft Fabric”