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spark-streaming

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risingwave

Best-in-class stream processing, analytics, and management. Perform continuous analytics, or build event-driven applications, real-time ETL pipelines, and feature stores in minutes. Unified streaming and batch. PostgreSQL compatible.

  • Updated Dec 5, 2024
  • Rust

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 Nov 21, 2024
  • Python

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 Dec 2, 2024
  • C#

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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