Understand the Kafka Connect framework and how it moves data between Kafka and external systems.

Compare Kafka Connect with custom producers and consumers for common integration workloads.

Learn how source connectors import data and sink connectors export Kafka records.

Explore database integration, change data capture, search indexing, data lakes, and other patterns.

Choose between Kafka Connect, Kafka Streams, producers, and consumers for different jobs.

Understand the worker, connector, and task hierarchy at the heart of Kafka Connect.

Compare local standalone deployments with scalable, fault-tolerant distributed workers.

Use Kafka Connect REST endpoints to create, inspect, update, pause, and delete connectors.

Learn how tasks scale work and how distributed workers recover from failures.

Install Kafka Connect, verify its scripts, and prepare a working local environment.

Configure and run a standalone worker with connector property files.

Configure a production-oriented distributed worker group and its internal topics.

Install third-party connector JARs and manage isolated plugin paths safely.

Practice the complete connector management lifecycle through REST calls.

See how connectors generate task configurations and distribute work.

Understand how source and sink progress is recorded, committed, and recovered.

Configure JSON, Avro, Protobuf, and primitive converters for keys and values.

Learn how Connect data and schemas support validation and controlled evolution.

Understand the config, offset, and status topics used by distributed workers.

Follow the source connector lifecycle from external polling to Kafka records.

Build a working pipeline that streams new lines from a file into Kafka.

Stream relational database rows using bulk, incrementing, timestamp, and combined modes.

Capture database changes from transaction logs with Debezium connectors.

Understand offset persistence, restarts, reprocessing, and recovery for source connectors.

Follow records from Kafka topics through sink tasks into external systems.

Build a working sink connector that writes Kafka records to a local file.

Write Kafka records into relational tables with inserts, upserts, deletes, and schema evolution.

Index Kafka records in Elasticsearch for real-time search and analytics.

Land partitioned Kafka data in Amazon S3 for data lake and analytics workloads.

Reason about at-most-once, at-least-once, exactly-once, retries, and idempotency.

Use lightweight transformations to reshape individual records inside connectors.

Apply built-in transforms for routing, filtering, field changes, timestamps, and keys.

Target the correct part of a Connect record and chain transforms predictably.

Build and test a practical multi-transform connector pipeline.

Implement, package, configure, and test a custom Java transformation.