Stream Processing Tools & Datasets for Python Data Engineering

Easy-to-use, powerful, and reliable system to process and distribute data, offering a web-based user interface for data flow management.

Distributed event streaming platform capable of handling trillions of events a day. Used for building real-time streaming data pipelines and applications with high-throughput, fault-tolerance, and scalability.

Framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Known for high performance in streaming data processing with exactly-once semantics.

Real-time computation system making it easy to process unbounded streams of data reliably. Fast and scalable distributed real-time computation framework for stream processing.

Stream processing library porting ideas from Kafka Streams to Python. Used for building high-performance and reliable real-time stream processing applications with Pythonic API.

Extension of Apache Spark API enabling scalable, high-throughput, fault-tolerant processing of live data streams. Integrated within Spark ecosystem for complex real-time data processing tasks.

Streaming data platform API-compatible with Apache Kafka but designed for better performance and easier operational management. Modern streaming platform for mission-critical workloads.

Advanced unified programming model for defining and executing data processing workflows that can run on any execution engine. Provides portability across multiple execution environments including Apache Flink, Apache Spark, and Google Cloud Dataflow. Ideal for building flexible, scalable data pipelines.