An open-source, cloud-native streaming database designed for real-time data processing and IoT applications.
HStreamDB is an open-source, cloud-native streaming database designed for real-time data processing, especially for IoT and modern data applications. It solves the problem of fragmented data stacks by integrating streaming storage, SQL-based processing, and real-time querying into a single system. This allows developers to build responsive applications that handle continuous data flows efficiently.
Data engineers, IoT developers, and backend engineers building real-time applications that require low-latency data ingestion, processing, and querying. It's also suitable for teams modernizing legacy message broker setups.
Developers choose HStreamDB for its unified approach to streaming data, combining the roles of a database and message broker with familiar SQL for processing. Its cloud-native, scalable architecture and built-in connectors reduce infrastructure complexity while ensuring high availability and performance.
HStreamDB is an open-source, cloud-native streaming database for IoT and beyond. Modernize your data stack for real-time applications.
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Subscriptions push stream updates instantly to applications, enabling responsive apps and replacing traditional message brokers, as highlighted in the README.
Provides built-in stream processing with SQL syntax for filtering, transformations, and joins, making it accessible to developers with database experience.
Separates compute and storage for independent scaling, using a Paxos-based consensus for high availability, per the README's description.
Easily integrates with external systems like MQTT, MySQL, and Redis via connectors, reducing integration effort as noted in the features.
The README admits 'More connectors will be added,' indicating the connector library is not yet comprehensive compared to mature alternatives.
Requires Docker and specific pre-requirements for development, with multiple steps in the quickstart, which can be barrier for rapid prototyping.
Relies on LogDevice for storage, introducing an additional layer that might add operational complexity and a learning curve for teams unfamiliar with it.