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  • Configure high concurrency mode for notebooks in pipelines . . .
    Session sharing with high concurrency mode is always within a single user boundary To share a single spark session, the notebooks must have matching spark configurations, they should be part of the same workspace, and share the same default lakehouse and libraries
  • Configure a Compute Context with Pre-started Compute Servers . . .
    We're going to look at a way to enable a pool of pre-started compute servers with just three commands Plus, the method we'll use can easily be modified to create other compute contexts with attributes and restricted access
  • How do I make sure my R session attached is using the compute . . .
    If your job script runs successfully, then it should run on a compute node You can always check your job status using squeue -u $USER It will tell you the node name so you can recognize which job is running on which node
  • Connect to pools | Databricks Documentation
    To attach a cluster to a pool using the cluster creation UI, select the pool from the Driver Type or Worker Type dropdown when you configure the cluster Available pools are listed at the top of each dropdown list
  • Compute Management in Fabric Environments - Microsoft Fabric
    With an environment, you have a flexible way to customize compute configurations for running your Spark jobs In an environment, you use the Compute section to configure the Spark session-level properties to customize the memory and cores of executors based on workload requirements
  • Basics of Databricks Workflows - Part 3: The Compute Spectrum
    There are three main configurations to choose from when defining compute resources for a specific workload: Cluster Type, Access Mode, and Databricks Runtime (DBR) All-purpose Compute is optimal for development, collaboration and interactive analysis It can be created via the user interface (UI), command-line interface (CLI), or REST API
  • A Beginner’s Guide to Spark Compute in Microsoft Fabric . . .
    Spark Compute enables data engineering and data science scenarios on a fully managed Spark compute platform that delivers unparalleled speed and efficiency Spark Compute is a way of telling Spark what kind of resources are needed for data analysis tasks





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