A curated list of awesome System Design (A.K.A. Distributed Systems) resources.
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Python Stream Processing
Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
The live data layer for apps and AI agents. Create up-to-the-second views into your business, just using SQL
Asynchronous Python framework for event-driven services. A thin client for Kafka, RabbitMQ, NATS, Redis and MQTT with full access to native broker features, plus AsyncAPI docs, in-memory tests and observability out of the box.
🦀 event stream processing for developers to collect and transform data in motion to power responsive data intensive applications.
Fast, Simple and a cost effective tool to replicate data from Postgres to Data Warehouses, Queues and Storage
Python Stream Processing. A Faust fork
HStreamDB is an open-source, cloud-native streaming database for IoT and beyond. Modernize your data stack for real-time applications.
Microservices library - scalecube-services is a high throughput, low latency reactive microservices library built to scale. It features: API-Gateways, service-discovery, service-load-balancing, the architecture supports plug-and-play service communication modules and features. built to provide performance and low-latency real-time stream-processing
Distributed batch data processing framework
Stream Processing Made Easy
Stateful actors on Apache Flink 2.x and Java 21 — durable per-key state, exactly-once messaging, Kafka and Kinesis I/O, Kubernetes-native deployment. Continues the Apache Stateful Functions programming model on the modern Flink line.