Kafka Connect for Data Integration and Streaming

Modern data architectures require the seamless movement of information between databases, search engines, and stream processing platforms. Kafka Connect provides the standardized framework to bridge these systems without writing complex custom code, enabling reliable and scalable data ingestion and export. This course provides a clear path to understanding how to move data in real time across your entire ecosystem. You will transition from understanding core architectural concepts to managing robust data pipelines that handle high-volume streams with fault tolerance. By focusing on configuration and ecosystem integration, you will gain the skills necessary to act as a bridge between disparate data silos and your streaming platform. What you'll learn: - Understand the architecture of Kafka Connect including workers, connectors, tasks, and converters. - Configure source connectors to ingest data from external systems and relational databases. - Deploy sink connectors to export data into destinations like PostgreSQL and ElasticSearch. - Manage connector lifecycles using the REST API for automated deployments and monitoring. - Differentiate between standalone and distributed modes to ensure high availability and scalability. - Apply modern best practices using Schema Registry integration to maintain data integrity across your pipelines. The course begins with foundational terminology and conceptual definitions before moving into the practical logic of configuring and scaling connectors. You will explore how to manage a distributed cluster and troubleshoot common integration patterns through written exercises. This course is designed for beginners interested in data engineering and system integration. No prior experience with Kafka Connect is required, though a basic understanding of data messaging is helpful. Start building reliable data pipelines today.