thatDot

thatDot Graph Event Stream Processing

Powering the next-generation of cybersecurity applications

<300mb

memory required

>1000eps

single CPU data throughput

>1000000eps

tested cluster data throughput (no upper limit)

<10μs

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.

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.

Help Center

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Novelty

Novelty for AWS

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Testimonials

What people are saying

thatDot Platform’s advanced Novelty Scoring for categorical data feature is the future of anomaly detection. Its speed powers our new real-time services while also significantly reducing false-positive findings for our customers.

Gery Szlobodnyik

CEO

The security industry is headed towards leveraging knowledge graphs of all the relevant evidence provided by threat groups, network indicators and endpoint artifacts to quickly identify and mitigate threats. The security companies that can most confidently extract relevant context from these graph and then immediately action on findings to mitigate threats will be the winners. This is exactly the problem Quine aims to address.

Evan Wright

Staff Data Scientist

See for yourself

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

thatDot Graph Event Stream Processing

Streaming graph data analyzer

Forget time windows

Answer deep questions immediately

Understand categories

Unify your data

Scale effortlessly

Develop rapidly