Blog
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.