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Create a Quine Icon Library with Python
Add some flair to your Quine streaming graph visualizations while learning about the API at the same time.
Add some flair to your Quine streaming graph visualizations while learning about the API at the same time.
Quine's standing queries, idFrom + deterministic labelling can be use to efficiently create any subgraph you need (e.g. sequence based) in real time. This makes alerts more timely and root cause analysis more efficient.
Quine 1.4.0 release includes improvements for scalability, stability, and supernode mitigation plus work key to reaching 1M events/sec.
Learn how Quine achieves groundbreaking performance for real-time complex event processing and how you can reproduce the results.
Real-time video observability presents a number of data engineering challenges that graph ETL can solve.
Quine streaming graph detects hard to find password spraying attacks for IAM providers and enterprises alike.
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.
Standing queries let you embed business logic in your real-time graph analytics workstream.