Apache Sedona™

Telemetry

Apache Sedona™

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Setup
Setup
- Overview
- Supported platforms
  Supported platforms
  - Sedona with Apache Spark
    Sedona with Apache Spark
    
    Modules
    
    Language wrappers
  - Sedona with Apache Flink
    Sedona with Apache Flink
    
    Modules
    
    Language wrappers
  - Sedona with Snowflake
    Sedona with Snowflake
    
    Modules
- Maven Central coordinate
- Install with Apache Spark
  Install with Apache Spark
- Install with Apache Flink
  Install with Apache Flink
  - Install Sedona Scala/Java
- Install with Snowflake
  Install with Snowflake
  - Install Sedona SQL
- Release notes
- Compile the code
Download
Programming Guides
Programming Guides
- Sedona with Apache Spark
  Sedona with Apache Spark
  - Spatial SQL app
  - Raster SQL app
  - Pure SQL environment
  - Spatial RDD app
  - Sedona R
  - Work with GeoPandas and Shapely
  - Map visualization SQL app
    Map visualization SQL app
    
    Scala/Java
    
    Use Apache Zeppelin
    
    Gallery
  - Performance tuning
    Performance tuning
    
    Benchmark
    
    Tune RDD application
    
    Storing large raster geometries in Parquet files
- Sedona with Apache Flink
  Sedona with Apache Flink
  - Spatial SQL app (Flink)
- Sedona with Snowflake
  Sedona with Snowflake
  - Spatial SQL app (Snowflake)
- Examples
  Examples
  - Scala/Java
  - Python
API Docs
API Docs
- Sedona with Apache Spark
  Sedona with Apache Spark
  - SQL
    SQL
    
    Quick start
    
    Vector data
    Vector data
    
    Constructor
    
    Function
    
    Predicate
    
    Aggregate function
    
    DataFrame Style functions
    
    Query optimization
    
    Reading Legacy Parquet Files
    
    Visualization
    Visualization
    
    SedonaPyDeck
    
    SedonaKepler
    
    Raster data
    Raster data
    
    Raster loader
    
    Raster aggregates
    
    Raster writer
    
    Raster operators
    
    Raster map algebra
    
    Raster visualization
    
    Raster affine transformation
    
    Parameter
  - RDD (core)
    RDD (core)
    
    Scala/Java doc
  - Viz
    Viz
    
    DataFrame/SQL
    
    RDD
  - Sedona R
- Sedona with Apache Flink
  Sedona with Apache Flink
  - SQL
    SQL
    
    Overview (Flink)
    
    Constructor (Flink)
    
    Function (Flink)
    
    Aggregator (Flink)
    
    Predicate (Flink)
- Sedona with Snowflake
  Sedona with Snowflake
  - SQL
    SQL
    
    Overview (Snowflake)
    
    Constructor (Snowflake)
    
    Function (Snowflake)
    
    Aggregate Function (Snowflake)
    
    Predicate (Snowflake)
Community
Community
- Community
- Contributor Guide
  Contributor Guide
  - Rules
  - Develop
- Committer Guide
  Committer Guide
- Publications
Use cases
Use cases
Apache Software Foundation
Apache Software Foundation
- Foundation
- License
- Events
- Sponsorship
- Thanks
- Security
- Privacy
- Telemetry

Telemetry

Apache Sedona uses Scarf to collect anonymous usage data to help us understand how the software is being used and how we can improve it. You can opt out of telemetry collection by setting the environment variable SCARF_NO_ANALYTICS or DO_NOT_TRACK to true on your local machine, or the driver machine of your cluster.

Scarf fully supports the GDPR and is allowed by the Apache Software Foundation privacy policy. The privacy policy of Scarf is available at https://about.scarf.sh/privacy-policy.

Last update: April 26, 2024 18:49:24