Hyprstream

I built Hyprstream to address the challenges of real-time data processing and provide a solution for rapidly querying streaming data as it's ingested. Here's why: Real-Time Querying on Ingest: Existing tools often fall short when it comes to querying data immediately upon ingestion. Hyprstream is designed to make streaming data instantly queryable, unlocking real-time insights without waiting for batch processing. Empowering AI/ML Pipelines: AI and ML workflows depend on up-to-date, high-frequency data. Hyprstream enables rapid querying and aggregation of streaming data, ensuring pipelines can access fresh metrics like running sums, averages, and counts for training and inference. Blazing-Fast Insights: Businesses and applications increasingly require low-latency analytics to make split-second decisions. Hyprstream bridges the gap by combining real-time ingestion with lightning-fast query capabilities. Unified, Scalable Workflow: Hyprstream eliminates the need for separate tools for ingestion, caching, and querying. Its seamless integration with Apache Arrow Flight and DuckDB enables a streamlined, scalable workflow for time-series and real-time analytics. Future-Proof Design: Written in Rust, Hyprstream is built for performance, reliability, and scalability. It integrates seamlessly with ADBC-compliant datastores, making it adaptable to existing ecosystems and ready for evolving data demands. By enabling rapid querying of streaming data, Hyprstream transforms how developers, data engineers, and scientists interact with real-time datasets. It reflects my vision of creating a platform that simplifies complexity while delivering unparalleled performance for modern data-driven applications.