Problem Solvers: Inside the Stack w/ Tiger Data

Industrial data serves as a critical commodity, requiring high-performance storage solutions to unlock value for AI training and operational insights. A side-by-side comparison between plain PostgreSQL and PostgreSQL with the TimeScaleDB extension reveals significant performance disparities, even at a modest scale of 60 million rows. While plain PostgreSQL relies on standard heap tables and manual indexing, TimeScaleDB utilizes hyper-tables and continuous aggregates to optimize data retrieval and maintenance. These architectural differences result in a four-to-fivefold performance improvement during query execution. Beyond technical benchmarking, the discussion highlights the shift toward agentic DevOps, where automated agents manage infrastructure and database backfilling, reducing the reliance on manual intervention. As manufacturing environments increasingly prioritize data-driven decision-making, transitioning to specialized time-series backends becomes essential for maintaining system responsiveness and scalability in complex industrial applications.