Streaming Graph ETL: Real-time Video Observability Simplified

Real-time video observability presents a number of data engineering challenges that graph ETL can solve.

Learn how Meroxa's Turbine makes it easy to connect data sources to Novelty Detector to create real-time anomaly detection with categorical data.

Categorical data is enormously useful but often discarded because, unlike numerical data, there were few tools available to work with it until graph DBs and streaming graph came along.

Quine streaming graph detects hard to find password spraying attacks for IAM providers and enterprises alike.

Learn how easy it is to use Quine streaming graph's idFrom function to deduplicate Kafka (or any other) event streams.

Quine reduces Splunk and New Relic costs by evaluating data as it arrives and making choices to store or discard based on the value of the data.

Quine 1.3.0 is out and includes features, recipes, and documentation all aimed at improved streaming graph usability.

As graph database adoption accelerates, new data infrastructures like streaming graph will emerge to eliminate the scale struggles of graph databases.

Standing queries let you embed business logic in your real-time graph analytics workstream.

Quine is a natural fit for Kafka data pipelines. Consume data from Kafka topics, publish processed data to Kafka topics.

Blog #5 in the Ingesting Data series. A step-by-step guide to adding Quine's high-volume graph analytics inline with your Kafka-based event streams.

This blog shows you how Quine streaming graph can ingest multiple log formats to create a single, unified streaming graph for real-time analysis.

Ingest multiple data sources into Quine in order create a single streaming graph. ETL basics and Cypher queries are covered.

As crypto currencies go mainstream, better techniques for protecting users from fraud are needed. Enter streaming graph.

A step-by-step guide to ingesting data into Quine from live internet streams. ETL basics and Cypher queries are covered.

Part one in a series connecting different data producers to Quine streaming graph. Use Cypher to create ingest queries (ETL).

Quine 1.2.0 release sees significant new features, recipes, and performance enhancements.

With the strategic investment from CrowdStrike, thatDot plans to accelerate development of Quine streaming graph and accelerate community adoption.

Stop approaching streaming data the same way you do persistent data. Teach the stream to tell you when something interesting happens.

A list of upcoming events plus links to videos of recent presentations, all introducing Quine streaming graph concepts.

Advice and key concepts about Quine streaming graph that will accelerate your development.

When it comes to event-driven applications, graph database users require a new approach: streaming graph.

Ryan Wright (CEO of thatDot) joins Joe Reis and Matt Housley on the TGIF! Let's Talk Data live show to chat about streaming graphs.

Quine Recipes make it dead easy to connect to your real-time event streams and turn them into stateful streaming graphs

thatDot Releases First Open Source Software to Combine Complex Event Streaming and Graph Data Technologies for Data Pipeline Engineers

Matthew Splett from Tripwire explains how he replaced complex SQL queries with succinct Cypher queries to process rollup data.

Bringing cloud-based data management into the enterprise data center, where much enterprise data still lives, is now simpler than ever.

Advanced: Finding a malicious employee is one of the toughest cyber-security challenges in the industry.

Learn how Quine streaming graph achieved a sustained rate of recording 425,000 records per second into our streaming graph.

Building on previous analysis, this blog investigate the use of categorical anomaly detection to identify multi-stage exploit campaigns in AWS CloudTrail logs.

Destructive attack campaigns succeed because they integrate new techniques or new hardcoded credentials to access and victimize their targets.

The move to the cloud represents new challenges for enterprise security teams. Use thatDot Novelty Detector to detect the attack quickly.

Data comes in two flavors: Numeric and Categorical. Numeric data is easy, it’s numbers. Categorical data is everything else.

The distributed nature of modern virtualized software architectures has created added complexity in the networking stack, making it difficult to attribute behavior to any single service.

thatDot’s real-time Novelty Detector for categorical has updated its data exploration and data transformation capabilities.

Traditionally, monitoring alerts are produced comparing metrics against thresholds to identify behavior outside the norm.

thatDot is excited to share the general availability of the world’s first system for real-time categorical anomaly detection.

We introduced the term “3D Data” as a mnemonic and a way to think about streaming data processing that incrementally builds toward human-level data questions.

Cloud architectures enable a new level of integration with 3rd party systems and data sources to deliver the services our users and customers are looking for.

Draw Connections, Define Data, Drill Down

Imagine if the next time your video streaming operations dashboard-of-choice warns you that 100 users experienced video start failures in the last minute,

That word… ”that.” It’s how you point with a word. What better way to express what you mean than by directly pointing?

thatDot, announces a $2M funding round led by Oregon Venture Fund (OVF), with participation by Hale Capital Partners and Galois, Inc.