Case Study: Augury achieves real-time, scalable machine monitoring and reduced management overhead with ScyllaDB

Augury Foresees a Bright Future with Scylla

Augury, a leader in machine health and predictive maintenance using IoT and AI, was collecting high-volume time-series data from sensors and running real-time and historical analytics. As their dataset outgrew MongoDB, Augury needed a horizontally scalable, low-maintenance data store; after evaluating Apache Cassandra they chose ScyllaDB (Scylla), a Cassandra-compatible alternative that avoided heavy dependencies like Zookeeper and better matched their access patterns.

ScyllaDB was validated in a proof of concept and Augury migrated diagnostics and analytics from MongoDB to Scylla, running three-node Scylla clusters in each data center (six nodes total). ScyllaDB now supports millisecond OLTP queries for UIs and APIs and high-parallel OLAP via the Spark connector and Beam, reducing infrastructure cost and complexity, lowering management overhead, and delivering stable, high-performance access to their time-series data.

Augury

Amit Ziv-Kenet

Backend Tech Lead