We scaled PostgreSQL to over 350 TB+ by leveraging TimescaleDB's features such as hypertables and continuous aggregates to handle massive amounts of query statistics data, ingesting tens of billions of records daily into a single database instance. We utilized columnar compression to store hundreds of TBs efficiently, enabling fast analytical queries on the users' side. The Insights database is powered by an "off-the-shelf" Timescale service with high-availability replicas, and we leveraged data tiering to keep hundreds of TBs accessible. Continuous aggregates simplified logic in constructing different time periods, streamlining data analysis and processing, while approximation algorithms included in Timescale's hyperfunctions simplified implementation. However, we faced challenges such as database observability limitations, snapshot-based views, and difficulties with schema modification operations at scale. Despite these pain points, the process of building Insights showed us how far our product can go, and we've added some things to our to-do list as engineers behind the product.