Responsive

We are happy to announce that we have completed another SOC2 Type 2 audit along with completing another successful penetration test against our cloud services. You can find the latest reports on our trust center: trust.responsive.dev

(3/3) Application upgrades were by far the #1 requested topic when we asked what the community would like to learn about earlier this year. We hope you find this latest post in our Kafka Streams 101 series helpful! www.responsive.dev/blog/topolog...

This will be the foundation for being able to branch @kafkastreams.bsky.social apps to support seamless blue/green deploys. It also allows you to branch a previous version of the state to debug an issue from the past. Powerful stuff coming to the world of stream processing!

Kafka Streams 101: Windows & Time! 🕰️ What's the difference between event, stream and wall clock time? 🪟 What are the four different types of windows? ⁉️ What are the important error messages and metrics, and what do they mean? 👉 Read the full lesson here: www.responsive.dev/blog/windows...

“These innovations collectively cement open source Kafka Streams as the best choice for building event driven applications, […]”

Is stream processing interesting because of the new apps it enables, or because it promises better data processing? I'm in the first camp and am proud of the major contributions Responsive has made to the application space. My reflections on what's next: www.responsive.dev/blog/respons...

It's said that Silicon Valley is special because the density of smart and motivated people leads to chance encounters that don't happen elsewhere. I can attest to that. Here's how a coffee resulted in @responsive.dev building a database optimized for stream processing in 8 months. (1/n)

Some problems are impossible to solve without stream processing: for instance, did you know that metronome.com leverages Kafka and Kafka Streams to deliver real time billing features like spend limits at scale? (1/3)

I am soon interviewing @apurvamehta.com from @responsive.dev and Yingjun Wu from @risingwave.bsky.social on all things stream processing. This is going to be an amazing episode. Feel free to ask any questions related to Stream processing and I will add the most interesting ones. Shoot!

Small feature drop, row-level TTL for Kafka Streams: "The ttl function can use either the key or value, or both, to compute the ttl for that row and override the default ttl. It's also possible to .. only expire specific records." docs.responsive.dev/reference/st...

Is it end of the road for RocksDB in stream processing? Disaggregated state is the clearly superior architecture, with @responsive.dev investing heavily in SlateDB.io while Flink 2.0 has forked RocksDB. Here's why we've bet on SlateDB for Kafka Streams: www.responsive.dev/blog/why-sla...

Great article. One additional point: if your app really needs low-latency and sophisticated responses to events, you need Kafka. For instance, many use cases in logistics (eg. order fulfillment), fintech (trade settlement), security tech (anomaly detection) need to be event driven and need Kafka.