Awesome GitHub RepositoriesStream Processing Engines

ClickHouse is a high-performance, columnar analytical database designed for real-time query execution and large-scale data aggregation. It functions as a distributed data warehouse capable of processing petabytes of information, while also providing an embedded engine that integrates directly into applications for native query capabilities without external dependencies. The system is built to handle high-throughput ingestion and complex analytical workloads, delivering millisecond-level latency for interactive dashboards and operational monitoring. The platform distinguishes itself through ad

This project is a command-line processor designed for the parsing, filtering, and transformation of structured data streams. It functions as a declarative programming environment that treats data as immutable streams, allowing users to perform complex structural modifications through the composition of small, reusable functions. By utilizing a recursive tree traversal engine, the system enables the navigation, inspection, and modification of deeply nested hierarchical data structures. The engine distinguishes itself through a stream-oriented architecture that processes input records one by on

Kafka is a distributed event streaming platform designed for capturing, storing, and processing real-time data streams across interconnected nodes. It functions as a distributed commit log, providing a fault-tolerant storage mechanism that records state changes sequentially to ensure data consistency and durability across distributed environments. The platform distinguishes itself through a partitioned commit log architecture that enables horizontal scaling and parallel processing of data streams. It integrates a stream processing engine for continuous transformations and aggregations, while

RocketMQ is a cloud-native distributed messaging platform and streaming engine. It functions as a distributed transactional queue that ensures atomicity between local transactions and message delivery, and serves as an MQTT IoT message broker to bridge lightweight device traffic into high-performance data streams. The system is distinguished by a Kubernetes-native architecture that decouples compute from storage to allow independent scaling of traffic and data retention. It utilizes a tiered storage model to offload older data to remote storage and employs quorum-based replication and automat

Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It functions as a SQL analytics platform and a streaming data ingestion engine, allowing for the analysis of large datasets with low latency to support interactive dashboards and high-concurrency operational workloads. The system integrates a streaming data ingestion engine that loads information via batch or streaming processes to enable immediate analysis of arriving data. It provides high-performance analytical processing to execute slice-and-dice queries on massive data volume

Benthos is a stream processing engine and data integration pipeline used for routing, transforming, and connecting data streams between diverse sources and sinks. It functions as event routing middleware and a change data capture tool, streaming real-time database modifications as discrete events for downstream processing. The system utilizes a declarative pipeline configuration, where data flow and processing logic are defined in a single static file. It features a specialized domain-specific language for mapping, filtering, and enriching data payloads, allowing for complex transformations w

Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis