In my wide wide-ranging conversation with InfluxData CEO Evan Kaplan at AWS re:Invent, he explained why time series data has become foundational for the modern enterprise—powered by an explosion of sensors, the rise of AI, and the need for increasingly autonomous systems.
He also detailed how specialized databases outperform general-purpose systems for fast, high-resolution data, why developers must increasingly start projects with time series in mind, and how InfluxData’s new InfluxDB 3.0 represents a complete architectural overhaul built for scale, performance, and real-time intelligence.
Kaplan highlighted the company’s important partnership with AWS, which has expanded InfluxData’s distribution while enabling AWS to support massive time series workloads across its ecosystem.
Main Takeaways
- Time series data: Time series data is exploding due to the rise of physical and virtual sensors, AI-driven automation, and IoT—making specialized databases essential for high-resolution, high-velocity workloads.
- Many applications: Modern enterprises increasingly rely on time series data to build autonomous and self-healing systems, from industrial automation to EV battery management to self-driving vehicles.
- InfluxData and AWS partnership: InfluxData’s partnership with AWS represents a successful new model for commercial–open source collaboration, expanding distribution while preserving community value.
- The new InfluxDB release: The new InfluxDB 3.0 is a full architectural rebuild featuring Rust, native SQL, infinite cardinality, separated compute and storage, and a built-in Python engine for real-time processing.
Key Quotes
“Sensors speak time series—that’s the lingua franca.”
Kaplan emphasized that every sensor, whether physical or virtual, generates time-ordered data, and the explosion of sensors across cars, buildings, networks, and wearables is driving the massive rise in time series workloads. As enterprises pursue automation and AI-driven intelligence, they need high-resolution snapshots of the physical world—something only specialized time series databases can deliver efficiently.
“If you want to build sophisticated systems, the more resolution you have, the better.”
He explained that high-resolution data is central to automation, AI, and autonomy. From self-driving cars to industrial robots, organizations must continuously instrument their systems, detect anomalies, and refine behavior. Time series data forms the backbone of this evolution, enabling systems that are increasingly adaptive and self-healing.
“AWS approached us with a novel model—they license our open source, pay us to run it, and we build value on top.”
Kaplan describes how InfluxData and AWS created a collaborative model that avoids the industry tensions seen when cloud providers monetize open source without contributing back. The partnership expands distribution for InfluxData while allowing AWS customers to run large time series workloads alongside services like Redshift, SageMaker, and Lambda.
“InfluxDB 3.0 is a fundamental rewrite—Rust, native SQL, infinite cardinality, and real-time Python processing.”
He outlined the major innovations in InfluxDB 3.0, including a Rust-based core for fault tolerance, native SQL for broader interoperability, unlimited cardinality to overcome historical scaling limits, and a built-in Python engine with sub-50-millisecond triggers. Together, these enhancements allow developers to build anomaly detection, forecasting, and predictive maintenance applications directly on time series data.
