Kafka Connect Integration

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Kafka Connect is a framework for building scalable, fault-tolerant data connectors that move data in and out of Kafka. It abstracts connector complexity (database polling, API pagination, error handling) so engineers deploy without writing boilerplate. Mastery takes 6-8 weeks. Senior practitioners earn 30-40% premium because they build reusable connectors that power entire data platforms. The 5% who can design high-throughput, low-latency connectors are highly sought after.

Kafka Connect Integration이란

Kafka Connect is a framework for building, deploying, and managing data connectors that move data between Kafka and external systems at scale. A connector is a plugin that abstracts the tedious work of polling databases, paginating APIs, handling errors, and managing offsets. You write a connector once (source or sink), configure it, and it runs independently, scaling horizontally across a cluster. Source connectors pull data from external systems (databases, APIs, message queues) into Kafka topics. Sink connectors push data from Kafka topics into external systems (data warehouses, caches, search engines, APIs). The framework handles distributed coordination, fault recovery, and schema management.

🔧 도구 & 생태계

Kafka ConnectKafka brokersSource connectorsSink connectorsSchema RegistryConfluent PlatformJDBC connectorDebeziumConnect REST APIDistributed modeStandalone modeOffset management

📋 시작하기 전에

Devops Ci Cd

💰 지역별 연봉

지역	주니어	미들	시니어
USA	$90k	$150k	$230k
UK	$55k	$90k	$140k
EU	$60k	$100k	$150k
CANADA	$95k	$155k	$240k

🎓 자격증

Confluent Kafka Connect Documentation Confluent Developer Associate Exam Debezium CDC for Event Streaming

❓ 자주 묻는 질문

What's the difference between source and sink connectors?

A source connector pulls data FROM external systems (database, API, file) into Kafka topics. A sink connector pushes data FROM Kafka topics into external systems (data warehouse, cache, search engine). Example: JDBC source connector tails a PostgreSQL table; Elasticsearch sink connector indexes Kafka messages into ES.

How do I handle schema evolution?

Use Confluent Schema Registry. Define schemas in Avro/Protobuf, store them centrally, version them. When a column is added to a source table, the connector auto-evolves the schema. Consumers read the schema from Registry and deserialize correctly. Without it, schema mismatches break pipelines.

What happens if a connector fails mid-pipeline?

Kafka Connect tracks offsets (positions in source) in Kafka topic or external store. On restart, connector resumes from last offset, not from the beginning. For exactly-once semantics, use idempotent sinks (e.g., upsert in target DB by unique key, not insert).

Can I run connectors in distributed mode?

Yes. Multiple Connect workers form a cluster, share workload, and coordinate via a central Kafka topic. Failure of one worker = others rebalance and take over its connectors. Distributed mode is production standard; standalone is dev-only.

How do I monitor connector health?

Query the REST API `/connectors/{name}/status` for task state (RUNNING/FAILED/PAUSED). Log to ELK or Prometheus. Alert on FAILED state. Measure lag: source position - consumer position. If lag > threshold (e.g., 10k messages), something's slow or broken.

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