!! Exciting news !! Quine Streaming Graph has been nominated for a Noonie Award for Best Open Source Project. Please vote for Quine!
Easily Combine Data Sources
Join streaming and batch data with out-of-order data
Standing Queries
Massively parallel efficient graph computation, run at the perfect moment, every time.
Multi-Way Joins at Scale
Match Nth-degree deep relationships in real-time.
Complete Data Version History
Track every change, and easily query any historical data.
Categorical Data
Ingestion of complex events, and pattern recognition for numeric and non-numeric data types.
Swappable Data Storage
Integrate with persistent data store of your choice.
No Time Windows
Join new events with months-old data immediately with a fast stateful graph.
Graph Data Model
Understand data semantic relationships as high-level attributes.
Out Of Order Data
Automatically resolve out-of-order data from multiple or heterogeneous data sources.
Fast Reads & Writes
Durable storage + in-memory processing breaks traditional limitations.
Matt Splett
Principle Engineer, Tripwire
"Using Quine, I replaced pages of complex custom logic and SQL queries with simple queries for the stream computed rollup value that updates at each underlying event change."
Jim Plush
Distinguished Engineer, CrowdStrike
“Quine represents a paradigm shift in online graph processing capabilities. By allowing data to react to itself as well as its relationships in real time, it gives you the capability to augment your graph on the fly and free up downstream consumers to react to changes without having to keep asking the same questions. This allows building more performant services with fewer resources.”
Kevin Baker
Principle Architect, Analog Devices
"Being a Kubernetes architecture we needed to investigate the relationship between multiple related Kafka event streams to identify optimization of our compute nodes and Kubernetes autoscaling configuration. Unlike traditional and expensive reference lookups in relational databases, Quine enables us to correlate our graph-like streaming data in real-time. This has really reduced the overhead for querying our data to determine critical optimization opportunities for our platform."
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