10 Popular Python Stream Processing Frameworks 2023

Developers widely use Python Stream processing to query ongoing data streams and respond to important events in timeframes ranging from milliseconds to minutes. Complex event processing, Real-time analytics, and streaming analytics are all closely linked to stream processing, which is now the preliminary framework for executing these use cases. 

Stream processing engines are runtime libraries that permit coders to write code to process streaming data with not having to deal with low-level streaming mechanics. Data were traditionally processed in batches based on a schedule or predefined point (for instance, each night at 1 am, every hundred rows, or every time the volume reached two megabytes). However, as data volumes and speeds have increased, more than batch processing is needed for many applications. Python Stream processing has evolved into a must-have feature for modern applications. For various use cases and applications, enterprises have turned to technologies that respond to data as it is created. Stream processing enables applications to respond to new data events as they happen. Unlike batch processing, which groups data and collects it at predetermined intervals, stream processing applications collect and process data when it is generated.

Python Stream processing is most commonly used with data generated as a series of events, such as IoT sensor data, payment processing systems, servers, and application logs. The two common paradigms are publisher/subscriber (also known as pub/sub) and source/sink. A publisher or source generates data and events, which are then delivered to a stream processing application. Here the data might be augmented, tested against fraud detection algorithms, or otherwise transformed before being sent to a subscriber or sink. Furthermore, all major cloud services, such as Tensorflow, Numpy, and Pytorch, have native services that simplify stream processing development on their respective platforms.

Check out the list below to find more popular Python stream-processing libraries for your applications: