Stream Processing Projects
Tools and frameworks for processing streaming data.
3 projects available
How to Choose the Right Stream Processing Framework for Python?
When choosing among Apache Kafka, Apache Flink, and Apache Spark Streaming: Opt for Kafka when you need a robust, high-throughput, distributed event streaming platform primarily for building real-time streaming data pipelines and applications. Choose Flink for applications that require stateful computations on data streams, particularly when you need strong consistency guarantees and low-latency processing. Use Spark Streaming when you want to leverage a unified framework for both batch and stream processing, particularly if you are already using Spark for batch jobs and want to extend its capabilities to streaming.
Real-Time Messaging with Apache Kafka
intermediateBuild event-driven data pipelines using Apache Kafka with the confluent-kafka Python client. Learn to produce and consume messages, handle topics, and create the foundation for real-time data streaming architectures.
Stream Processing with Apache Flink
advancedProcess unbounded data streams using Apache Flink and PyFlink. Learn stateful computations, event-time processing, and windowing operations - essential for building sophisticated real-time analytics and continuous ETL pipelines.
Python Stream Processing with Faust
intermediateBuild Pythonic stream processing applications using Faust, a library designed specifically for Python developers. Learn async stream processing, stateful operations, and how to create real-time data pipelines with familiar Python syntax.