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Decodable wants to mainstream real-time stream processing • TechCrunch

decodablethe well funded platform for real-time data engineering and stream processing based in part on the Apache Flash open-source project, is today launching a major update that aims to make the service more attractive to large enterprises. Besides expanding the library of connectors to allow data ingestion from more sources and a new business support option, the main feature here is the launch of the automated job sizing capabilities. As a result, Decodable can now dynamically configure workloads to optimize performance and cost.

As Decodable founder and CEO Eric Sammer told me, he believes that while stream processing itself is quickly becoming mainstream — with Apache Flink becoming the de facto standard — what’s going on around it isn’t quite mainstream yet, in part because so far only companies with highly advanced technical teams were able to build on this technology.

“The analogy I’m thinking of is network switches,” he explained. “We can move packages back and forth. The next iteration of that — the part that I don’t think is fully mainstream yet — is the processing power on top of that — the ability to transform and aggregate. Which I think we would have called streaming analytics 10 years ago.”

Image Credits: decodable

Companies like Lyft, Uber, or Stripe are able to create this enterprise-level abstraction layer on top of these real-time data streams. Others, he argues, need a tool like Decodable to do that, especially if they want to build consumer-facing applications.

“Streaming is one piece of technology,” he also noted. “It’s a collection of Debezium plus Kafka or Redpanda or whatever – plus Flink, plus APIs and all that other stuff. And it’s priceless to set up a team and operationalize that. We add value there. And that’s why we focus on developer experience and self-service and enterprise features.”

As for the new task sizing feature, Sammer noted that users can tell the service how many tasks they want to dedicate to a given workload. Decodable will then scale up to this maximum number of tasks – or scale down, if warranted. For many users, this can lead to lower spending overall. And while that may also mean they won’t spend as much on Decodable, Sammer believes that if the company can make stream processing easier and more cost-effective for its users, those users will bring more workload to the service. “There are very few cases where you don’t want newer, more accurate data — or make a better decision in real time,” he said. “So from that perspective, every time we make it cheaper for someone to run a workload on Decodable, they’re adding more workloads.”

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