Best Event Stream Processing Software for Python
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Compare the Top Event Stream Processing Software that integrates with Python as of June 2025
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This a list of Event Stream Processing software that integrates with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.
What is Event Stream Processing Software for Python?
Event stream processing software enables organizations to analyze and process data in real-time as it is generated, providing immediate insights and enabling quick decision-making. This software is designed to handle large volumes of streaming data, such as sensor data, transaction logs, social media feeds, or financial market data. Event stream processing software often includes features like real-time analytics, pattern detection, event filtering, and aggregation to identify trends or anomalies. It is widely used in applications such as fraud detection, predictive maintenance, supply chain management, and real-time analytics. Compare and read user reviews of the best Event Stream Processing software for Python currently available using the table below. This list is updated regularly.
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Ably
Ably
Ably is the definitive realtime experience platform. We power more WebSocket connections than any other pub/sub platform, serving over a billion devices monthly. Businesses like HubSpot, NASCAR and Webflow trust us to power their critical applications - reliably, securely and at serious scale. Ably’s products place composable realtime in the hands of developers. Simple APIs and SDKs for every tech stack, enable the creation of a host of live experiences - including chat, collaboration, notifications, broadcast and fan engagement. All powered by our scalable infrastructure.Starting Price: $49.99/month -
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Pathway
Pathway
Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a scalable Rust engine based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with Docker and Kubernetes. -
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Arroyo
Arroyo
Scale from zero to millions of events per second. Arroyo ships as a single, compact binary. Run locally on MacOS or Linux for development, and deploy to production with Docker or Kubernetes. Arroyo is a new kind of stream processing engine, built from the ground up to make real-time easier than batch. Arroyo was designed from the start so that anyone with SQL experience can build reliable, efficient, and correct streaming pipelines. Data scientists and engineers can build end-to-end real-time applications, models, and dashboards, without a separate team of streaming experts. Transform, filter, aggregate, and join data streams by writing SQL, with sub-second results. Your streaming pipelines shouldn't page someone just because Kubernetes decided to reschedule your pods. Arroyo is built to run in modern, elastic cloud environments, from simple container runtimes like Fargate to large, distributed deployments on the Kubernetes logo Kubernetes.
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