In this lesson we will:

  • Discuss some of the main stream processing frameworks.

Stream Processing Frameworks

To mitigate the complexity associated with stream processing, a number of developer frameworks and platforms have been created. Some of the most popular ones include:

  • Apache Flink: An open-source platform for distributed stream processing that supports batch processing, event-time processing, and data streaming;

  • Apache Kafka Streams: An open-source platform for processing streaming data in a way which is tightly integrated with Apache Kafka;

  • Apache Storm: A real-time distributed computing system that can process large amounts of data in real-time and provides fault-tolerance and scalability;

  • Apache Spark Streaming: A scalable and fault-tolerant processing system that enables real-time processing of large streams of data using micro-batch processing;

  • Amazon Kinesis: A managed platform that enables real-time data processing and analytics on streaming data at scale;

  • Google Cloud Dataflow: A fully-managed cloud service for building and deploying stream processing pipelines that supports batch and stream processing;

  • Microsoft Azure Stream Analytics: A real-time analytics service that provides near real-time processing of data streams and enables insights to be derived from data in motion.

These frameworks make it easier for developers to implement stream processing solutions in an accurate, reliable and robust way.

Next Lesson:

Stream Processing vs Real Time Data Warehouses

In this lesson we will contrast stream processing with performing real time analytics in a data warehouse.

0h 15m

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