The event stream processing platform for developers. Unified experience for real-time data ingestion, stream processing, and low-latency serving. Best-in-class performance and cost-efficiency. Supports SQL and Python.

A Flexible and Powerful Parameter Server for large-scale machine learning

酷玩 Spark: Spark 源代码解析、Spark 类库等

基于Spark的电影推荐系统，包含爬虫项目、web网站、后台管理系统以及spark推荐系统

.NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers.

scala、spark使用过程中，各种测试用例以及相关资料整理

Wormhole is a SPaaS (Stream Processing as a Service) Platform

C# and F# language binding and extensions to Apache Spark

An open source framework for building data analytic applications.

Streaming System 相关的论文读物

Real Time Analytics and Data Pipelines based on Spark Streaming

Scala examples for learning to use Spark

Stream computing platform for bigdata

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

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.

Big Data Processing Framework - Unified Data API or SQL on Any Storage

Enabling Continuous Data Processing with Apache Spark and Azure Event Hubs

Spark, Spark Streaming and Spark SQL unit testing strategies

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

Self-contained examples of Apache Spark streaming integrated with Apache Kafka.