Scalable Anomaly Detection with Kafka and Cassandra

Paul Brebner wraps up a series on anomaly detection at scale:

The complete machine for the biggest result (48 Cassandra nodes) has 574 cores in total.  This is a lot of cores! Managing the provisioning and monitoring of this sized system by hand would be an enormous effort. With the combination of the Instaclustr managed Cassandra and Kafka clusters (automated provisioning and monitoring), and the Kubernetes (AWS EKS) managed cluster for the application deployment it was straightforward to spin up clusters on demand, run the application for a few hours, and delete the resources when finished for significant cost savings. Monitoring over 100 Pods running the application using the Prometheus Kubernetes operator worked smoothly and gave enhanced visibility into the application and the necessary access to the benchmark metrics for tuning and reporting of results.

The system (irrespective of size) was delivering an approximately constant 400 anomaly checks per second per core.

This is a good summary of what was an interesting series.

Related Posts

Databricks Runtime 5.5

Bilal Aslam and Yifan Cao announce Databricks Runtime 5.5: Secrets API in R notebooksThe Databricks Secrets API [Azure|AWS] lets you inject secrets into notebooks without hardcoding them. As of Databricks Runtime 5.5, this API is available in R notebooks in addition to existing support for Python and Scala notebooks. You can use the dbutils.secrets.get function to obtain […]

Read More

Building an Image Classifier with PyTorch

Rogier van der Geer shows how you can use PyTorch to build out a Convolutional Neural Network for image classification: The tool that we are going to use to make a classifier is called a convolutional neural network, or CNN. You can find a great explanation of what these are right here on wikipedia. But we […]

Read More


April 2019
« Mar May »