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  • Managed Service for Apache Spark
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Overview Guides Reference Samples Resources
Google Cloud Documentation
  • Documentation
    • More
    • Overview
    • Guides
    • Reference
    • Samples
    • Resources
  • Console
  • Overview
  • Key Concepts
  • Managed Service for Apache Spark serverless
    • Overview
    • Managed Service for Apache Spark serverless tiers
  • Managed Service for Apache Spark on clusters
  • Compare serverless and cluster deployments
  • Managed Service for Apache Spark on GKE
  • Get started
  • Serverless
    • Create a serverless Spark batch workload
  • Clusters
    • Create a cluster
    • Submit a Spark job to a cluster
    • Spark tutorials
      • Use Gemini to develop Spark applications
      • Analyze public datasets with Spark
      • Sentiment analysis with Spark MLlib
  • GKE
    • Run a Spark job on Kubernetes
  • Develop
  • Serverless
    • Configure serverless
      • Use custom containers
      • Use GPUs
      • Use Dynamic Workload Scheduler
      • Network configuration
      • Spark runtime versions
        • Overview
        • Spark runtime version 3.0
        • Spark runtime version 2.3
        • Spark runtime version 2.2
        • Spark runtime version 1.2
      • Service accounts
      • Spark properties
      • Staging bucket
    • Create batch workloads and sessions
      • Create a serverless Spark batch workload
      • Create serverless interactive sessions and session templates
      • Use the serverless Spark Connect client
      • Use JupyterLab for serverless batch and notebook sessions
      • Use serverless templates
        • Overview
        • Cloud Spanner to Cloud Storage
        • Cloud Storage to BigQuery
        • Cloud Storage to Cloud Spanner
        • Cloud Storage to Cloud Storage
        • Cloud Storage to JDBC
        • Hive to BigQuery
        • Hive to Cloud Storage
        • JDBC to BigQuery
        • JDBC to Cloud Spanner
        • JDBC to Cloud Storage
        • JDBC to JDBC
        • Pub/Sub to Cloud Storage
    • Create an Apache Iceberg table with metadata in Lakehouse runtime catalog
    • Transform data and write to Apache Iceberg
    • Use the BigQuery connector with Spark
      • Overview
      • Query BigQuery tables
    • Run PySpark code in BigQuery Studio notebooks
    • Optimize
      • Autoscale workload resources
      • Autotune Spark workloads
      • Use Lightning Engine
        • Accelerate batch workloads and sessions with Lightning Engine
        • Run the Native Query Execution qualification tool
      • Serverless Spark solution accelerators
  • Clusters
    • Data processing
      • Configure Spark
        • Manage Spark dependencies
        • Customize Spark environment
        • Enable concurrent writes
        • Enhance Spark performance
        • Tune Spark
        • Use Lightning Engine
      • Run Spark jobs
        • Use the console
        • Use the command line
        • Use the REST APIs Explorer
          • Create a cluster
          • Run a Spark job
          • Update a cluster
          • Delete a cluster
        • Use client libraries
      • Run Hadoop jobs
      • Write and run Spark Scala jobs
      • Run Trino
      • Run Flink
      • Run Hive
      • Run Pig
      • Run HBase
      • Run Python
        • Configure the Python environment
        • Use Cloud Client Libraries for Python
      • Use data connectors
        • Use the Spark BigQuery connector
          • Overview
          • BigQuery connector code samples
        • Use the Cloud Storage connector
        • Use the Spark Spanner connector
    • Data lakes and lake houses
      • Explore and extract data
      • Transform data
      • Load data into BigQuery
      • Create a lakehouse with Spark
      • Configure metastores
      • Create an Apache Iceberg table with metadata in BigLake metastore
      • Use Iceberg
      • Use Delta
      • Use Hudi
    • Data science notebooks and UIs
      • Use notebooks
        • Overview
        • Run a Jupyter notebook on a cluster
        • Run a genomics analysis on a notebook
        • Use the JupyterLab extension to develop serverless Spark workloads
      • Use the Component Gateway
    • Data sources and storage
      • Connect to data sources
      • Connect to Cloud Storage
      • Connect to BigQuery
      • Connect Hive to BigQuery
      • Connect to Bigtable
      • Connect to Pub/Sub Lite
    • Administration and data governance
      • Fleet management
      • Lineage
        • Enable Spark data lineage
        • Enable Hive data lineage
    • Configure clusters
      • Components
        • Overview
        • Delta Lake
        • Docker
        • Flink
        • HBase
        • Hive WebHCat
        • Hudi
        • Iceberg
        • Jupyter
        • Pig
        • Presto
        • Ranger
          • Install Ranger
          • Use Ranger
            • Use Ranger with Kerberos
            • Use Ranger with caching and downscoping
            • Back up and restore a Ranger schema
        • Solr
        • Trino
        • Zeppelin
        • Zookeeper
      • Cluster images
        • Overview
        • Services
        • Cluster image versions
          • Overview
          • 3.0.x release image versions
          • 2.3.x release image versions
          • 2.2.x release image versions
          • 2.1.x release image versions
      • Compute options
        • Machine types
        • GPUs
        • Minimum CPU platform
        • Flexible VMs
        • Secondary workers
        • Local solid state drives
        • Boot disks
        • Attached hyperdisks
    • Create clusters
      • Overview
      • Configure a network
        • Overview
        • Use secure tags
        • Configure Private Service Connect
      • Select a cluster region