When people discuss building data pipelines using Apache Kafka, they are usuallly referring to a couple of use cases. The first is building a data pipeline where Apache Kafka is one of the two end points. For example, getting data from Kafka to S3 or getting data from MongoDB into Kafka. The second use case involves building a pipeline between two different systems but using Kafka as an intermediary. An example of this is getting data from Twitter to Elasticsearch by sending the data first from Twitter to Kafka and then from Kafka to Elasticsearch.
When we added Kafka Connect to Apache Kafka in version 0.9, it was after we saw Kafka used in both use cases at LinkedIn and other large organizations. We noticed that there were specific challenges in integrating Kafka into data pipelines that every organization had to solve, and decided to add APIs to Kafka that solve some of those challenges rather than force every organization to figure them out from scratch.
The main value Kafka provides to data pipelines is its ability to serve as a very large, reliable buffer between various stages in the pipeline, effectively decoupling producers and consumers of data within the pipeline. This decoupling, combined with reliability security and efficiency, makes Kafka a good fit for most data pipelines.
Some organizations think of Kafka as an end point of a pipeline. They look at problems such as “How do I get data from Kafka ...