Client Overview

Anodot is a leading data analytics company that leverages artificial intelligence and machine learning for real-time business monitoring and anomaly detection. Their platform analyses billions of data points per day, providing clients with advanced insights and visualisations that enable faster, smarter decision-making.

‍The Challenge

For Anodot, Apache Cassandra underpinned the vast data flows that powered its AI-driven platform. Running Cassandra at the scale required to process billions of data points daily required specialised expertise in configuration, tuning, and ongoing operational management. Without that expertise, there was a risk of instability, costly inefficiencies, and difficulties in managing multiple clusters across AWS infrastructure.

The Solution

Digitalis.io partnered with Anodot to provide deep expertise in scaling, securing, and maintaining Cassandra in production. The team worked closely with Anodot to fine-tune cluster configuration and performance, support version upgrades, and define monitoring and alerting approaches tailored to the platform’s scale. AWS-native architecture and Infrastructure-as-Code practices were used to ensure consistency, resilience, and automation in deployments.

At the same time, Digitalis embedded AxonOps, its Cassandra management and observability tool, to give Anodot full visibility across clusters, automated repair capabilities, robust alerting, and a simplified management workflow. This combination of hands-on operational support and powerful management tooling enabled Anodot to maintain a stable, scalable environment capable of supporting its AI analytics workloads at scale.

The Outcomes

Through this partnership, Anodot achieved a far more resilient and robust Cassandra environment, backed by daily operational support from Digitalis.io. By stabilising Cassandra operations, Anodot’s internal team gained the confidence that their core database platform was being run by proven experts, freeing them to focus on product innovation and client delivery. The introduction of AxonOps further strengthened reliability while reducing the complexity of managing multiple clusters at production scale, ensuring performance, efficiency, and long-term cost optimisation.

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