In modern data architectures, real-time data movement across systems — databases, message queues, object stores, and more — is critical for operational analytics, replication, and lakehouse ingestion.

Kafka Connect is a robust and scalable tool designed to simplify the creation, deployment, and management of data pipelines using Apache Kafka. It enables source-to-Kafka and Kafka-to-sink streaming with built-in fault tolerance and minimal custom code.

This post explores the architecture and capabilities of Kafka Connect, its core components, and how to build reliable and manageable data pipelines for your enterprise data ecosystem.

What is Kafka Connect?

Kafka Connect is a framework for connecting Kafka with external systems using source and sink connectors:

  • Source Connectors: Pull data from external systems into Kafka topics.
  • Sink Connectors: Push data from Kafka topics into external systems.

It is part of the Kafka ecosystem and supports:

  • Distributed and standalone modes
  • Schema management via Confluent Schema Registry
  • Offset tracking, exactly-once semantics, and automatic retries

Kafka Connect Architecture

Kafka Connect has a pluggable architecture composed of:

[External System] ← Source Connector ← Kafka Connect Workers → Kafka Broker → Sink Connector → [Target System]

Key components:

  • Workers: JVM processes running tasks
  • Connectors: Configuration + logic for integration
  • Tasks: Parallelized execution units
  • Offset Storage: Tracks progress across tasks
  • Transforms: Lightweight data manipulation between ingestion and write

Kafka Connect can be run in:

  • Standalone mode: Ideal for development or simple pipelines
  • Distributed mode: Supports horizontal scaling, fault tolerance, and central REST configuration

Setting Up a Kafka Connect Cluster (Distributed)

Example distributed configuration (connect-distributed.properties):

bootstrap.servers=broker1:9092,broker2:9092
group.id=connect-cluster
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
config.storage.topic=connect-configs
offset.storage.topic=connect-offsets
status.storage.topic=connect-status

Start workers using:

connect-distributed connect-distributed.properties

Register connectors via REST API:

curl -X POST http://localhost:8083/connectors -H "Content-Type: application/json" -d @my-connector.json

Building Pipelines with Source and Sink Connectors

Example: MySQL → Kafka → S3

  • Use Debezium MySQL Source Connector to capture CDC
  • Use S3 Sink Connector to persist to Amazon S3

Source config (Debezium MySQL):

{
"name": "mysql-source",
"config": {
"connector.class": "io.debezium.connector.mysql.MySqlConnector",
"database.hostname": "mysql",
"database.port": "3306",
"database.user": "user",
"database.password": "pass",
"database.server.id": "184054",
"database.server.name": "dbserver1",
"table.include.list": "inventory.customers",
"database.history.kafka.bootstrap.servers": "kafka:9092",
"database.history.kafka.topic": "db-history"
}
}

Sink config (S3):

{
"name": "s3-sink",
"config": {
"connector.class": "io.confluent.connect.s3.S3SinkConnector",
"topics": "dbserver1.inventory.customers",
"s3.bucket.name": "my-data-lake",
"s3.region": "us-east-1",
"format.class": "io.confluent.connect.s3.format.json.JsonFormat",
"flush.size": "1000",
"storage.class": "io.confluent.connect.s3.storage.S3Storage"
}
}

Ensuring Fault Tolerance

Kafka Connect provides built-in mechanisms for reliability:

  • Distributed mode: Restores failed tasks automatically
  • Offset tracking: Stored in Kafka to prevent duplicate processing
  • Dead Letter Queues (DLQs): Capture and route failed records
  • Retry policies: Customize retries and backoffs for transient issues
errors.tolerance=all
errors.deadletterqueue.topic.name=dlq-topic
errors.deadletterqueue.context.headers.enable=true
errors.log.enable=true
errors.log.include.messages=true

Monitoring and Management

Monitor Kafka Connect using:

  • REST API (/connectors, /status, /tasks)
  • JMX metrics for CPU, memory, throughput, lag
  • Confluent Control Center (for enterprise setups)
  • Prometheus + Grafana exporters

Common REST commands:

  • List connectors: 
    curl localhost:8083/connectors
  • Check status: 
    curl localhost:8083/connectors/mysql-source/status

Best Practices

  • Use distributed mode for production deployments
  • Keep Kafka topics compacted if used for metadata or offsets
  • Tune task.max and batch sizes for throughput
  • Secure with SSL/TLS, basic auth, or mTLS
  • Use transforms for lightweight data enrichment
  • Monitor offset lag and DLQs for anomaly detection

Conclusion

Kafka Connect is an essential tool for building reliable, scalable, and maintainable data pipelines in modern architectures. Whether you’re replicating databases, loading data into cloud storage, or synchronizing systems in real-time, Kafka Connect enables rapid development with minimal code and built-in resilience.

With the right set of connectors, error handling, and monitoring practices, you can confidently stream and integrate data across systems — powering everything from analytics to automation.