Case Study: Glooko achieves 95% compression and 480x faster queries with Tiger Data

Glooko cuts costs 40% and speeds queries 480x with Timescale

Glooko, a diabetes monitoring platform used by over one million patients, faced significant challenges scaling its time-series data infrastructure. Their system was ingesting over 100 million new data points daily, which led to sluggish ingestion, data bloat, slow queries for clinicians, and rising costs on their existing document database. To address this, Glooko partnered with Timescale and implemented its Tiger Data service on AWS.

By migrating to Timescale's Tiger Data, a managed Postgres platform optimized for time-series, Glooko unified its architecture on a single database. The solution provided automatic partitioning, policy-based compression, and retention. The results were substantial, including compression ratios up to 97%, query speeds that became 480 times faster, and an overall reduction in costs by 40%, all while enabling more stable ingestion of their massive daily data load.

Glooko