Time series databases unlock real-time data for robotics

Episode 254 of The Robot Report Podcast featured Doug Pagnutti, industrial developer advocate at Tiger Data, discussing how time series databases unlock real-time data capabilities for robotics and industrial automation. Pagnutti brings deep domain expertise to the conversation: he has spent more than 12 years working in oil and gas and manufacturing automation, with hands-on experience developing and managing PLCs (programmable logic controllers), robots, and SCADA (supervisory control and data acquisition) systems. After this operational background, he moved into industrial software development, working at Dell EMC and VTScada before joining Tiger Data. In his current role, he helps teams build real-world industrial systems and bridges the gap between operational technology and information technology teams by advising on time-series data infrastructure for industrial IoT deployments at scale. The podcast segment, which aired at the 21:20 mark, centered on TimescaleDB, a time series database created by Tiger Data that extends PostgreSQL with time-series primitives, columnar storage, and automatic partitioning. According to the sponsor description, these features allow queries to remain fast on live data without requiring data pipelines, migration, or a separate database system—a common pain point as growing Postgres databases encounter performance bottlenecks on real-time workloads. Pagnutti discussed practical approaches to integrating these databases with various sensors, managing data at scale across distributed systems, and optimizing performance both in cloud and edge environments—key considerations for modern robotics and industrial automation deployments.

The episode addresses a growing infrastructure challenge in industrial robotics and IIoT. As robots and automated systems collect real-time sensor data from PLCs, SCADA systems, and edge devices, the volume and velocity of time-series data can overwhelm traditional database architectures—a problem Pagnutti has encountered across oil, gas, and manufacturing sectors. Time series databases like TimescaleDB solve this by extending PostgreSQL with specialized primitives (columnar storage, automatic partitioning) designed for the access patterns of time-stamped data, eliminating the need for teams to maintain multiple database systems or expensive data pipelines. Pagnutti's 12+ years bridging the operational technology (OT) and information technology (IT) gap positions him to advise automation engineers on scaling IIoT deployments—a critical need as industrial organizations digitize their edge infrastructure.

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