See Quine in Action: 3 Live Demos Showing Graph ETL Use Cases

Nothing Beats a Live Demo

Over the last three weeks, we’ve been fortunate enough to deliver presentations at events hosted by DataStax (makers of Apache Cassandra), Confluent (makers of Apache Kafka), and the PDX Video Tech Meetup (sponsored by AWS Elemental). Each video includes a live demo showing Quine in action and includes ways for you to follow along and go further. Enjoy!

DataStax Hands-On Workshop: Password Spray Detection

I joined the team at DataStax to demonstrate Quine graph ETL in action using the Password Spray Detection recipe. In this hands-on workshop, we cover how to use Quine with AstraDB, DataStax’s Cassandra-as-a-Service DB. If you recall, we used the Cassandra persistor for our performance tests where Quine broke 1 million events/second (read the blog describing the reproducible tests here).

You can access the Github repo with the recipe and the excellent and comprehensive README here: https://github.com/datastaxdevs/workshop-streaming-graph-quine

Confluent Current22 Demo: Advanced Persistent Threat Use Case with Apache Kafka

Ryan Wright (@rrwright) delivered a bite-sized demonstration of how to use Quine to detect APT attacks. At 10 minutes, this demo packs a lot of useful information into a lightning talk format and points to many of the features that make streaming graph ETL an essential tool for cybersecurity solutions.

Slides and Kafka resources are available here, including more info on how to add Quine graph ETL to Apache Kafka data pipelines.

PDX Vid Tech Meetup: Real-time Video CDN Root Cause Analysis

Rob Malnati (@robmalnati) and Allan Konar (@7evenbridges)presented a live demonstration using Quine to ingest, transform, sessionize, and analyze log data from CloudFront, AWS Elemental, and Mux client APIs. Video QoE/S issues were identified in real time and root cause analysis notifications were generated automatically. This example uses AWS Kinesis for the event stream feed.

We will be publishing the recipe for this soon but a related recipe — CDN Cache Efficiency — is available now to try. You can also read about Kinesis integration here.

Download and Try

If you want to try it on your own logs, here are some resources to help:

1. Getting Started Guide
2. Download Quine – JAR file | Docker Image | Github
3. Check out the Ingest Data into Quine blog series covering everything from ingest from Kafka to ingesting .CSV data

And please don’t hesitate to sign up for Quine community slack. There are lively discussions and it is a great place to get fast answers to pressing question. You can find me there or on Twitter (@michaelaglietti).

Thanks!