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SQL stream processing, analytics, and management. We decouple storage and compute to offer efficient joins, instant failover, dynamic scaling, speedy bootstrapping, and concurrent query serving.

  • Updated Jul 16, 2024
  • Rust

Data Accelerator for Apache Spark simplifies onboarding to Streaming of Big Data. It offers a rich, easy to use experience to help with creation, editing and management of Spark jobs on Azure HDInsights or Databricks while enabling the full power of the Spark engine.

  • Updated Jul 15, 2024
  • C#

Generate relevant synthetic data quickly for your projects. The Databricks Labs synthetic data generator (aka `dbldatagen`) may be used to generate large simulated / synthetic data sets for test, POCs, and other uses in Databricks environments including in Delta Live Tables pipelines

  • Updated Jul 15, 2024
  • Python

A complete example of a big data application using : Kubernetes (kops/aws), Apache Spark SQL/Streaming/MLib, Apache Flink, Scala, Python, Apache Kafka, Apache Hbase, Apache Parquet, Apache Avro, Apache Storm, Twitter Api, MongoDB, NodeJS, Angular, GraphQL

  • Updated Feb 1, 2019
  • TypeScript

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