Real-time graph ETL for modern Data Pipelines with Quine and NoSQL

Join us for a workshop where you learn by doing. We use Quine and Astra DB, built on Apache Cassandra, to work on a practical use case of detecting a password spraying attack, and help to keep your users safe!

Most websites have a method of locking down an account after a few bad password attempts in a short time. But what about many attempts spread out over a longer larger time frame?  In this workshop we will work with a sample of identity management event logs to detect password spraying.

Quine is the open source streaming graph developed by the team at thatDot. Quine provides the real-time graph ETL tools required for event data analysis and fraud detection. Enterprises need to detect complex cyber attacks as they happen, even if the events themselves are spread out over long time intervals, and Quine can help you do that. 

Persisting high volume, “low signal” data in realtime is hard for SQL or relational databases. Detection would require complex JOINs, if detection was even possible at all, and often requires use of time windows. This is why persisting Quine data, like the log-in events in this workshop’s sample dataset, is a perfect fit for Astra DB. Astra DB brings the power of Cassandra to the Quine case, with none of the operational headaches.

Agenda

• A hands-on (learn by doing) example of how to deal with sustained hacker attacks
• Using Streaming/Real-time Graph with more examples beyond Password Spraying attack.
•  Learn how Astra is suitable for persisting High-volume, low-signal data