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11/10/2022

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Home | Quine, Open Source Streaming Graph for Event-Driven Applications

by John Doe

!! Exciting news !! Quine Streaming Graph has been nominated for a Noonie Award for Best Open Source Project. Please vote for Quine!Easily Combine Data SourcesJoin streaming and batch data with out-of-order dataStanding QueriesMassively parallel efficient graph computation, run at the perfect moment, every time.Multi-Way Joins at ScaleMatch Nth-degree deep relationships in real-time.Complete Data Version HistoryTrack every change, and easily query any historical data.Categorical DataIngestion of complex events, and pattern recognition for numeric and non-numeric data types.Swappable Data StorageIntegrate with persistent data store of your choice.No Time WindowsJoin new events with months-old data immediately with a fast stateful graph.Graph Data ModelUnderstand data semantic relationships as high-level attributes.Out Of Order DataAutomatically resolve out-of-order data from multiple or heterogeneous data sources.Fast Reads & WritesDurable storage + in-memory processing breaks traditional limitations.READ THE DOCSMatt SplettPrinciple 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 PlushDistinguished 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 BakerPrinciple 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."Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam aliquam amet, sociis eu lorem sed rutrum. Condimentum augue erat iaculis magna morbi cum ac gravida.Feature 1Feature 2Feature 3Feature 4Feature 5Feature 1Feature 2Feature 3Feature 4Feature 5Feature 1Feature 2Feature 3Feature 4Feature 5

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!! 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.

READ THE DOCS

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."

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam aliquam amet, sociis eu lorem sed rutrum. Condimentum augue erat iaculis magna morbi cum ac gravida.

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