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They want to connect those topics to external systems like S3 or just write some SQL queries to understand what’s in that topic.”
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They want to create topics, and they want to send and receive messages from the topic. “What users care about is data,” Narkhede said. One of the harder problems they had was building a true “pay for what you stream” experience. So all of your backend systems can account for your time, and you are billed for what you truly use vs. So Confluent built a control plane around Kafka to automate limit handling. When you start work on elastic scaling, Narkhede said, you are constantly running into limits the cloud infrastructure has on cloud abstractions, e.g., the number of connections that can be made at the time. The third fundamental change didn’t really have to do with Kafka at all. The Confluent Cloud can scale elastically up to 100 mg/second (reads and writes) without having to plan anything or talk to anyone, essentially providing a “no cluster” experience. Next, elastic scaling takes away the need to size clusters to accommodate spikes in service. This, she said is “so you can scale and still maintain several nines of uptime.” With services like Amazon Web Services sharing containers and services, the Confluent Cloud built security around multitenancy architecture, along with instituting elastic quotas. The first big change was to solve for multitenancy and add the ability to manage quotas in the system.
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Head to /apm for a free 14-day trial.įundamental changes were also needed to the Kafka architecture to leverage economies of scale to provide elasticity essential to being fully cloud native. Next, they created a whole ecosystem of tools, from connectors to different systems to a stream processing layer, to a way to manage your schemas.ĭoes your current APM solution lack the actionable details you need to debug issues fast? Raygun’s APM offers unrivaled insights into the performance of your software. They added security around Kafka workloads so you can deploy it in real enterprises for real workloads, she explained. So the company’s engineers took a year or so to build a cloud native, fully managed service that developers can use in the public cloud. “What we know about enterprises is they want to buy the whole car,” said Narkhede. It provides a new foundation for data that can bring data from all those different varieties of services in one place so you can consume it at a large scale, she said.
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It lives in the data infrastructure layer alongside relational database and/or modern no-SQL databases, and alongside data warehouses. Kafka sits above the operation layer and below the application layer in the stack. But as cloud technology is expanding, some fundamental changes were necessary to make Apache Kafka truly cloud native. With over 60% of the Fortune 100 relying on Apache Kafka, the service has become both popular and entrenched. In this episode of The New Stake Makers podcast, we are joined by Neha Narkhede, chief technology officer and co-founder of Confluent and one of the co-creators of Apache Kafka. Also available on Apple Podcasts, Google Podcasts, Overcast, PlayerFM, Pocket Casts, Spotify, Stitcher, TuneIn