How we leverage Antithesis to deterministically simulate our entire SaaS platform and verify its correctness, all the way from signup to running entire Kafka workloads.

Benchmarking databases – and maintaining fairness and integrity while doing so – is a notoriously difficult task to get right, especially in the data streaming space. Vendors want their systems to produce mouth watering results, and so unnatural configurations divorced from customer realities (AKA “vanity” benchmarks) get tested, and it's ultimately the end-user that is left holding the bag when they realize that their actual TCO is a lot higher than they were led to believe.

A huge part of building a drop-in replacement for Apache Kafka® was implementing support for compacted topics. The primary difference between a “regular” topic in Kafka and a “compacted” topic is that Kafka will asynchronously delete records from compacted topics that are not the latest record for a specific key within a given partition.

Serverless products and usage based billing models go hand in hand, almost by definition. A product that is truly serverless effectively has to have usage based pricing, otherwise it’s not really serverless!

Middleware is a cloud observability company that uses AI to help customers identify, understand, and fix issues across their infrastructure. It’s probably not a surprise to you that their entire business relies on scalable streaming infrastructure: they’re ingesting and processing tens of TiBs of telemetry every day, and need to process it very efficiently.

The future of modern data infrastructure is object storage.