Announcing Open Source Release of Quine Streaming Graph

thatDot avatar Rob Malnati

The World’s First Streaming Graph for Categorical Data

We at thatDot are pleased to announce that today the release of Quine, our streaming graph for event processing, as an open source project. Quine’s unique approach combines graph data and streaming technologies into a modern, developer-friendly open source software package. For the first time, teams can process categorical data in real time without resorting to encoding methods.

Developers and data pipeline engineers use Quine to rapidly build high volume, real-time, complex event processing workflows at scale. A handful of Quine queries can replace months of development time and millions in costs, eliminating batch processing, multi-level joins, time windows, and other time-consuming and outdated processes that drag down and stall analysis on streaming data.

“Enterprise data engineering teams are confined to the limitations and tradeoffs of the previous generation of event processing frameworks like Flink. They spend enormous time and effort building complicated event-driven architectures that only work on small time-windows of in-memory data and miss out on the bigger picture,” said Ryan Wright, the creator of Quine and Founder/CEO of thatDot. “Quine can transform months of tedious data engineering into an afternoon’s work enabling data pipeline engineers to easily interpret high-volume event data streams, innovate and ship products  faster, and to use the emerging Graph AI tools driving the next wave in machine learning.”

Community Created, Pre-built Recipes for Common Workflows

Early access launch partners, community members, and contributors have already created pre-built application functions called “recipes” —to package up valuable use cases for one-click operation. These include:

  • Blockchain Real-time Tag Propagation – Ingests Ethereum and propagates dirty money tags to trace money laundering.
  • CDN Cache Efficiency Analysis – Continuously monitor CDN logs to materialize cache efficiency by PoP, Geography, and ASN, generating alerts and tracing root cause.
  • Apache Server Log Observability – Ingests Apache server events and observes event lineage between services.

“thatDot’s Quine is a powerful new tool for anyone building event-driven applications. Standing queries let us match complex patterns as data arrives as well as query the past shape of data without the restriction of time windows.” Roy Hodgman, Data Science Manager, Rapid7

Quine is freely available  on GitHub and directly from the Quine Community website. Quine is part of thatDot’s portfolio of event processing solutions. Elements of thatDot’s solutions were  instrumental to DARPA’s cybersecurity research for insider threat detection and stopping Advanced Persistent Threats (APTs) .

Register for the upcoming conferences, webinars or recordings showcasing Quine: