As data pours in from every corner of the digital world, the race to process, scale and unlock real-time insights has never been fiercer. Recognizing this relentless demand, Amazon Web Services Inc. and InfluxData Inc. have struck a powerful alliance to reinvent the way developers and enterprises harness time series data.

Their partnership blends the agility of open-source innovation with the muscle of cloud-native architecture, promising to supercharge performance, scalability and simplicity for data-driven organizations worldwide, according to Brad Bebee (pictured, right), general manager of Amazon Neptune and Timestream at AWS.

“We offer a number of different managed open source databases,” he said. “With Influx, I think one of the things that’s unique about the relationship is that in addition to the open-source version, the Influx 2.x that we offer as part of our managed service, now with the launch of Read Replicas, customers who love the open source can also get some additional functionality for Read Replicas … which allow you to expand your read throughput and increase your availability features.”

Bebee and Evan Kaplan (left), chief executive officer of InfluxData, spoke with analyst John Furrier, during an exclusive conversation on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how the AWS-InfluxData partnership provides a native, scalable and cost-effective solution for time series data, empowering developers, enterprises and AI-driven applications to unlock new levels of performance and efficiency. (* Disclosure below.)

The expanding role of time series data in AI and IoT

Time series databases are critical in an era dominated by artificial intelligence and the internet of things. The demand for real-time data processing is growing as industries seek to harness insights from sensor data, network telemetry and industrial IoT applications, according to Kaplan.

“In terms of use cases, our business is lots of … let’s call it sensor analytics,” he said. “Let’s call it both virtual sensors and physical sensors, network telemetry, space, rocketry, energy, process controls, industrial IoT — things that have sensors attached, things that require high ingest, super performing query data that’s basically in motion, and the foundational data for instrumenting systems.”

A significant breakthrough in this partnership is the introduction of Read Replica, a feature designed to address the challenges of scaling time series workloads. Before this innovation, enterprises had to rely on costly, redundant “hot standby” instances to achieve high availability. Read Replica solves this by allowing businesses to increase their read throughput without doubling infrastructure costs, according to Bebee.

“With Read Replica, they can choose to configure a Read Replica instance and get the benefit of increased read throughput,” he said. “They can basically double the number of queries that they can process, but they no longer have to pay for that sort of hot standby because that second Read Replica also handles that failover mode. It’s for developers and people going into production; it’s a more cost-effective way to provide more throughput and scalability for their time series applications.”

The integration of InfluxDB with AWS also addresses one of the most pressing challenges in AI-driven data systems: managing real-time data at the edge while leveraging cloud-based intelligence. Businesses are increasingly deploying AI models that require both immediate on-device decision-making and cloud-based learning from vast datasets. This integration equips them with the needed real-time decision-making, cloud-enabled AI training and seamless data flow, Kaplan added.

Here’s theCUBE’s complete interview with Brad Bebee and Evan Kaplan:

(* Disclosure: Amazon Web Services Inc. sponsored this segment of theCUBE. Neither AWS nor other sponsors have editorial control over content on theCUBE or SiliconANGLE) 

Photo: SiliconANGLE