The Rise of Real Time
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Multi-tenancy allows a single instance of software to serve multiple customers. It is an architecture where a single instance of the software runs on a server and serves multiple tenants. This allows for efficient use of computing resources and reduces maintenance costs as updates only need to be applied to a single code base.
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The Rise of Real Time
- 14. Multi-tenancy
Editor's Notes
- Really excited to be here.
- People at this conference are part of something big. Won’t be quite Woodstock—it’s one day of Kafka and stream processing not 3 days of love, and I’m not sure what our CoC says about LSD. But nonetheless there is a big change happening in the world—there is something happening here—and I think we’re really lucky to be here at the beginning of it.
- The heart of it is that what business are is changing. A business used to be something that was built out of paper and people.
- Now a business is something that is equally made out of data and software (with lot’s of great people doing the hard bits!) This is really different. So what does a fully digital business look like?
- What does a fully digital business look like? We’re still in the process of really learning how to build one. One of the nice things about my job is I get to talk to people who have built this kind of fully digital company, lot’s of them here in silicon valley, but also I get to talk to people who are in the process of business that were originally built out of people and paper and which are transitioning.
- The early versions of this process mirrored what humans did. You get data systems replace the file cabinets as a storage mechanism, and computer programs that replace some of the human processes. But early on it is piecemeal and grounded in the constraints and thought patterns of what came before—more a horseless carriage than—than a modern car.
- It will be no surprise to anyone in this room that I think a big part of what this natively digital business looks like is about streams. Data on paper is inherently static, but that limitation is long gone and in a fully digital world an event that occurs in one part of an organization needs to be available everywhere immediately. Piles => Streams Processing and reacting to these streams is at the heart of what businesses do.
- You can take a concrete example of a type of business we all know—Retail. Sales and shipments aren’t a static thing, they happen all the time, and reacting to these streams is at the heart of what a retail company does. This is by no means unique to retail: maybe 30-50% of what companies do falls into this domain of reacting to streams of events.
- This is very much why we built Apache Kafka. We had a very particular vision for what a company would look like if you reimagined it’s use of data around streams of events.
- The vision that inspired Kafka was this idea of a streaming platform. A central place where every part of the organization could publish the stream of data it had, and that would let every other thing
- 0.9
- Core: Data pipeline Venture bet: Stream processing
- Kafka has been around the longest and only at 0.9! Storm - Windowing - Small State Spark Streaming 2.0 - Flink - RocksDB-based state - Strong guarantees - True streaming
- The vision that inspired Kafka was this idea of a streaming platform. A central place where every part of the organization could publish the stream of data it had, and that would let every other thing