If you’re an IT executive or software engineer at an enterprise of any reasonable size, the chances are that you’ve heard about Apache Kafka. However, whether you’ve implemented it—or even fully grokked the benefits it can bring to your data analytics, application architecture, and overall business strategies—is another question altogether.
The inaugural episode of Intersect’s “Cloud Native in 15 Minutes” podcast gives you the information to help you answer the latter question, straight from Kafka co-creator and Confluent co-founder Neha Narkhede. In the time it takes for a short commute to the office, she discusses the rise of Kafka and the growing importance of streaming data and event-driven architecture. Narkhede also explains how Kafka relates to other cloud-native strategies such as Kubernetes, and provides some great examples of how Kafka is used in the wild inside both traditional enterprises and digital-first companies like Lyft.
Here are a couple of quotes from the episode where Narkhede explains Kafka in the context of other, broader technology initiatives.
Kafka enables many initiatives
“I think we are getting to experience a really interesting period in technology, where there are several really significant paradigm shifts playing out all at the same time … real adoption of the public cloud; breaking the monolith into microservices; the rise of mobile-first, real-time customer experiences; Internet of Things; or the move from batch to real-time. The most interesting thing about Kafka—and I think the reason for its steep rise in adoption—is that it is the common thread that enables all of those paradigm shifts.”
The reason for event-driven architecture
“Now that every company sees themselves as a technology and a data company, you really have to take a new look at how data is treated in a company. It’s no longer treated as just something that goes to rest and is queried when you need it. It actually needs to be treated as streams of information as they happen in the real world, that can be studied and reacted to as that happens.”
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Learn more about Kafka and event-streaming
If you’re interested in learning more about Kafka and streaming data overall, check out the following resources: