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thatDot Graph Event Stream Processing

Powering the next-generation of cybersecurity applications
memory required
single CPU data throughput
tested cluster data throughput (no upper limit)
query response time
What thatDot does

Streaming graph data analyzer

  • Forget time windows

    Cyber threat detection is often hamstrung by cybersecurity application time windows. Catch advanced persistent threats, insider threats, and more with unbounded threat intelligence analysis.

  • Answer deep questions immediately

    Shift analysis left into the data stream without persisting in a database first. Analyze risk, catch cyber intruders, prevent data breaches, detect fraud, all in a graph format made to answer those deep questions.

  • Understand categories

    Get instant complex pattern detection on categorical data such as names, places, and IP addresses, early in the data pipeline, without first changing the data to sparse, bloated numeric data.

  • Unify your data

    Find relationships in multiple streaming data sources at once (Apache Kafka, Kinesis, SQS,…) and with batch files. Resolve duplicates and intelligently filter out unneeded information in real time.

  • Scale effortlessly

    Handle millions of events per second, even on standard hardware or cloud instances.

  • Develop rapidly

    Rich APIs and standard Cypher graph query language make embedding streaming graph event stream processing in your applications straightforward.

How it works

thatDot streaming graph analytics and machine learning anomaly detection

Query the future with thatDot Streaming Graph

Streaming Graph simultaneously queries multiple data streams from different sources letting you find patterns without time window limitations. This makes it uniquely well-suited to finding advanced persistent threats as well as other cybersecurity threats. It’s also exceptional at real-time anomaly detection, risk analysis, fraud detection, and edge optimization.    
Screen capture of thatDot Streaming Graph showing an advanced persistent threat detected in CDN data.

Find hidden problems with thatDot Novelty

Novelty has a unique built-in pattern learning AI that looks for anything unusual in your data as it streams in. No training or data labeling is required and the event stream processing engine powered by open source Quine provides answers 1000 times faster than algorithms like Isolation Forest. Use it for cybersecurity to find insider threats. It also works well for smart filtering on the edge, finding well-hidden fraud, or just finding anomalies you didn’t know to look for.
thatDot integrates

Drop thatDot smoothly into your stack

Integrate easily with your existing streaming data architecture.

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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.