You are viewing documentation for an older version of Confluent Platform. For the latest, click here.
This section describes Kafka Connect, a component of open source Apache Kafka. Kafka Connect is a framework for connecting Kafka with external systems such as databases, key-value stores, search indexes, and file systems.
Connect makes it simple to use existing connector implementations for common data sources and sinks to move data into and out of Kafka. A source connector can ingest entire databases and stream table updates to Kafka topics. It can also collect metrics from all of your application servers into Kafka topics, making the data available for stream processing with low latency. A sink connector can deliver data from Kafka topics into secondary indexes such as Elasticsearch or batch systems such as Hadoop for offline analysis.
Kafka Connect is focused on streaming data to and from Kafka, making it simpler for you to write high quality, reliable, and high performance connector plugins. It also enables the framework to make guarantees that are difficult to achieve using other frameworks. Kafka Connect is an integral component of an ETL pipeline when combined with Kafka and a stream processing framework.
Kafka Connect can run either as a standalone process for running jobs on a single machine (e.g., log collection), or as a distributed, scalable, fault tolerant service supporting an entire organization. This allows it to scale down to development, testing, and small production deployments with a low barrier to entry and low operational overhead, and to scale up to support a large organization’s data pipeline.
The main benefits of using Kafka Connect are:
- Data Centric Pipeline – use meaningful data abstractions to pull or push data to Kafka.
- Flexibility and Scalability – run with streaming and batch-oriented systems on a single node or scaled to an organization-wide service.
- Reusability and Extensibility – leverage existing connectors or extend them to tailor to your needs and lower time to production.