Building event-driven applications the easy way in Go.
- watermill
- event-driven
- event-sourcing
- sagas
Repository Index
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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.
- java
- hazelcast
- in-memory
- big-data
Drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads.
Project Flogo is an open source ecosystem of opinionated event-driven capabilities to simplify building efficient & modern serverless functions, microservices & edge apps.
- flogo
- iot
- integration
- golang
The fastest unified streaming SQL Engine in a single C++ binary. ⚡ Millisecond latency. 100+ GB/s throughput. Continuously compute stateful context from streams, logs, metrics, events, and CDC.
- clickhouse
- sql
- redpanda
- single-binary
Ruby and Rails efficient Kafka processing framework
- karafka-framework
- kafka
- kafka-client
- kafka-clients
Probabilistic data structures for processing continuous, unbounded streams.
- bloom-filter
- go
- stable-bloom-filters
- cuckoo-filter
A platform that makes it easy for developers to build realtime, cost-effective, operations-focused applications
High performance Rust stream processing engine seamlessly integrates AI capabilities, providing powerful real-time data processing and intelligent analysis.
- flow
- rust
- rust-lang
- stream
Trill is a single-node query processor for temporal or streaming data.
- streaming-data
- temporal-data
A compatibility layer for event-driven abstractions
- stream-processing
- future
- clojure
- async
Esper Complex Event Processing, Streaming SQL and Event Series Analysis
- cep
- complex-event-processing
- open-source
- compiler
Build platforms that flexibly mix SQL, batch, and stream processing paradigms
- golang
- event-sourcing
- stream-processing
- brokers
A stream processing framework to build stateless applications on Kafka
- kafka-streams
- clojure
- framework
The database purpose-built for stream processing applications.
- stream-processing
- real-time
- sql
- interactive
Real-time decision features without streaming infra. Turn live events into product reflexes — no Kafka, no Flink, no feature store.
- analytics
- feature-store
- fraud-detection
- python
Haskell distributed stream processing with exactly-once semantics
Sliding-window and regular top-K sketches, based on HeavyKeeper
- go
- golang
- heavy-hitters
- probabilistic-data-structures