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thatDot Streaming Graph

Powering the Next Generation of Cybersecurity

Analyze streaming data with the pattern, category, and relationship power of graph analysis.

Screen capture of thatDot Streaming Graph showing an advanced persistent threat detected in CDN data.
Product Overview

Combine the speed of an event stream processor, with the power of graph analysis.

Developed by DARPA for advanced persistent threat (APT) detection, funded by Crowdstrike, thatDot Streaming Graph takes in multiple data streams at once, searches for important patterns formed from past data, data streaming past now, and data that hasn’t arrived, then pushes forward the key insights, the dangerous needles in the data haystack.

  • Ask Categorical Questions

    Instead of first converting categorical data into bloated numerical data, do deep analysis on IP addresses, people, things, etc. directly.

  • Integrate Quickly

    Use as both a source and a sink for Apache Kafka, Kinesis, SQS pipelines, and more. Pluggable, interchangeable storage options – Cassandra, Clickhouse, …

  • Compute at High Speed

    Read and write data fast, and handle out of order data with parallelized distributed compute.

  • Speed Development

    Answer tough relationship, anomaly, and pattern questions with the standard Cypher graph query language and robust APIs.

  • Query Across Time

    Find patterns, anomalies, and problems without time windows. Query your entire historical data set plus current data in real-time.

  • Scale Even on Supernodes

    Get unlimited scale – tested throughput of over 1 mill events/second. Supernode problem solved – tested to 100’s of millions of edges.

  • What Its For

    A new approach to anomaly detection

    As data flows in from multiple sources, use Streaming Graph to interpret that data as a graph, to find duplicates, relationships, and categories across the data streams. Set up a standing query that finds the pieces to the puzzle as they flow by. The moment the last piece clicks into place for an anomaly or pattern you've indicated, that data is pushed forward into a new data stream immediately. The mean time to answer (MTTA) can be measured in microseconds.

  • Cybersecurity - Threat detection in real-time

    Time window software limitations have left our agencies, companies, and infrastructure utilities wide open to attack by bad actors, foreign and domestic, willing to find a way in and wait till those time windows expire. DARPA helped develop Quine, the open source heart of Streaming Graph, so you can spot insider threats, advanced persistent threats (APTs), etc. no matter how much time passes before exfiltration. Catch them in the act, the moment they start stealing data or damaging internal systems, not days or even months later after the damage is done.

    Learn more.

  • Edge - Smart filter data and optimize networks

    Match up disparate device data streams with contextual data that may be stationary, resolve duplicates, intelligently filter out the unimportant, and analyze patterns of behavior for problems, bottlenecks, etc. Shift analysis left into the data stream itself to shorten Mean Time to Answer (MTTA).

    Learn more.

  • Financial - Risk analysis and fraud prevention

    As data from point of sale devices, ATMs, etc flows in, letting it get prepped and dropped in a stationary database before you can analyze it delays fraud analysis until it's too late to prevent, delays risk analysis until it may be too late to adjust your portfolio without loss. Spot fraudulent patterns or shifts in risk exposure within microseconds, while the data is still flowing in.

    Learn more.


How It Works

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Read the docs

Get the help documentation, getting started guides, and recipe examples.

View videos

See Streaming Graph in action, and see how it’s used.

Join our community

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See for yourself

If you think Streaming Graph or Novelty might be for you, contact us to see them in action.