Kafka is an open-source program for storing, reading, and analyzing streaming data. It is open-source, which means it’s free-to-use amongst a big community of users and developers contributing to new features, upgrades, and support on a regular basis. Kafka can run on multiple servers as a distributed system, allowing it to take advantage of each server’s processing power and storage capacity.

Learning Apache Kafka doesn’t have to be difficult. Read on to get a friendly explanation of the Apache Kafka fundamentals.

Apache Kafka provides an unified, high-throughput, low-latency platform for handling real-time data feeds. Installing Apache Kafka, especially the right configuration of Kafka Security including authentication and encryption is kind of a challenge. This should give a brief summary about our experience and lessons learned when trying to install and configure Apache Kafka, the right way.

So here we are…the final chapter. In Part 2 of this series, we started to drill down into some of the concepts that make Kafka great. We concluded that although terminology between MQTT and Kafka was similar (for example topics), they behaved quite differently under the hood. We also took a brief overview of Kafka Connect and how we can use some of the enterprise connectors to stream our data to other platforms. Yet we did learn that Kafka does have some shortfalls.

Hulu, the entertainment streaming platform, needed a solution to scale up its internal application and infrastructure monitoring platform as it grew beyond 1 million metrics per second. The solution it created combines two open source tools— InfluxDB, a time series database, and Kafka, an event-streaming platform. It’s not just global enterprises like Hulu that have access to world-class tools and infrastructure to achieve their business goals.