Resources
Categorical Data: An Untapped Source of Real-Time Insights
Categorical data is an oft-ignored source of valuable business intelligence. Quine makes it easy to process categorical data with your existing ETL pipeline.
Why Digital Twins Need to Go Real Time
For digital twins to be truly useful in business operations, they must be able to drive actions and do so the instant an issue emerges, perhaps even beforehand.
What is the difference between batch and stream processing?
Stream processing offers the opportunity to detect important patterns in information and act in real time.
Scaling Quine Streaming Graph to Process 1 Million Events/Second
Learn how Quine achieves groundbreaking performance for real-time complex event processing and how you can reproduce the results.
Streaming Graph ETL: Real-time Video Observability Simplified
Real-time video observability presents a number of data engineering challenges that graph ETL can solve.
Are You Ready for Low and Slow Auth Attacks?
Quine streaming graph detects hard to find password spraying attacks for IAM providers and enterprises alike.
What’s the difference between Categorical and Numerical 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.
Kafka data deduping made easy using Quine’s idFrom function
Learn how easy it is to use Quine streaming graph’s idFrom function to deduplicate Kafka (or any other) event streams.
Save Big on SIEM Storage Costs Using Quine’s Semantic ETL
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.
Drive Streaming Event Workflows with Standing Queries
Standing queries let you embed business logic in your real-time graph analytics workstream.
Understanding the Scale Limitations of Graph Databases
As graph database adoption accelerates, new data infrastructures like streaming graph will emerge to eliminate the scale struggles of graph databases.
Network Log Analysis Using Categorical Anomaly Detection
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.
Reducing False Positive Alerts With Contextual Anomaly Detection
Traditionally, monitoring alerts are produced comparing metrics against thresholds to identify behavior outside the norm.
Where Quine Streaming Graph Fits In Kafka-Based Data Pipelines
Quine is a natural fit for Kafka data pipelines. Consume data from Kafka topics, publish processed data to Kafka topics.
Ingest How-To: Real-time Graph Analytics for Kafka Streams with Quine
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.
Modernizing ETL For Cloud
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.
Ingest and Analyze Log Files Using Streaming Graph
This blog shows you how Quine streaming graph can ingest multiple log formats to create a single, unified streaming graph for real-time analysis.
Ingesting From Multiple Data Sources into Quine Streaming Graph
Ingest multiple data sources into Quine in order create a single streaming graph. ETL basics and Cypher queries are covered.
Real-time Blockchain Monitoring is Hard without A Streaming Graph
As crypto currencies go mainstream, better techniques for protecting users from fraud are needed. Enter streaming graph.
Ingesting data from the internet into Quine Streaming Graph
A step-by-step guide to ingesting data into Quine from live internet streams. ETL basics and Cypher queries are covered.
Building a Quine Streaming Graph: Ingest Streams
Part one in a series connecting different data producers to Quine streaming graph. Use Cypher to create ingest queries (ETL).
Time Series Streaming Graph and other Quine 1.2.0 Highlights
Quine 1.2.0 release sees significant new features, recipes, and performance enhancements.
Your Graph DB Won’t Scale? Stop Querying it.
Stop approaching streaming data the same way you do persistent data. Teach the stream to tell you when something interesting happens.
Key Concepts to Help You Get Started With Streaming Graph
Advice and key concepts about Quine streaming graph that will accelerate your development.
The Evolution To Streaming Graph from Graph Databases
When it comes to event-driven applications, graph database users require a new approach: streaming graph.
Let’s Talk Streaming Graph! (with demo)
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.
Recipe for Streaming Graph Success
Quine Recipes make it dead easy to connect to your real-time event streams and turn them into stateful streaming graphs
Computing Recursive Rollups in a Kafka Event Streaming Pipeline
Matthew Splett from Tripwire explains how he replaced complex SQL queries with succinct Cypher queries to process rollup data.
AWS Names thatDot’s Novelty Detector As A Containers Anywhere Partner
Bringing cloud-based data management into the enterprise data center, where much enterprise data still lives, is now simpler than ever.
Stop Insider Threats With Automated Behavioral Anomaly Detection
Advanced: Finding a malicious employee is one of the toughest cyber-security challenges in the industry.