Kafka Connect is a connector for scalable, reliable streaming of data between Kafka and other systems that makes moving large collections of data in and out of Kafka faster and easier. Kafka Connect provides a relatively mature and stable basic framework for DataPipeline, and also provides some out-of-the-box tools, which greatly reduces R&D investment and improves application quality.
Below, we take a look at the specific advantages of Kafka Connect.
First, Kafka Connect provides a business abstraction centered on the data pipeline. There are two core concepts in Kafka Connect: Source and Sink. Source is responsible for importing data to Kafka, and Sink is responsible for exporting data from Kafka. They are both called Connectors. For example, Source Connector and Sink Connector actually provide a high degree of business abstraction for data reading and writing, which can simplify the management of many life cycles.
Of course, the Source Connector will initialize the Source Task, and the Sink Connector will initialize the Sink Task. These are standard packages. For data, the data structure is standardized and abstracted through Source & Sink Record. In addition, when enterprise customers are doing data integration, data requires a certain format in many application scenarios, so Schema Registry & Projector is used in Kafka Connect to solve the problem of data format verification and compatibility. When the data source changes, a new Schema version will be generated, and the Projector will be used to complete the compatibility of the data format through different processing strategies.
Second, Kafka Connect has good scalability and fault tolerance. These features are in line with Kafka. In the stream processing and batch processing modes, it depends more on how the source side reads data. Kafka Connect naturally supports stream processing and batch transmission methods. Both single node and cluster horizontal scaling capabilities are directly supported by the Kafka Connect framework. In terms of task recovery and state maintenance, the writing progress information of the destination task is automatically managed by the Kafka Connect framework, and the source task can put the reading progress information into Kafka as needed, saving a lot of energy to manage the progress of the task after restarting.
For a general application scenario like data integration, everyone definitely doesn't want to reinvent the wheel. At present, under the Kafka Connect ecosystem, there are a total of 84 connectors that can be used directly, most of which are open source. Some of them are officially provided by Kafka, others are certified by Confluent, and some are provided by third parties. After appropriate tailoring according to the needs, these Connectors can be applied to their own system platforms.
For more questions about Kafka Connect and real-time data integration, please follow the DataPipeline WeChat public account, or visit the official website directly: http://datapipeline.com .