Apache Kafka has become the go-to platform for real-time event streaming. One of its most powerful extensions is Kafka Connect — a framework for scalable, fault-tolerant data integration. Kafka Connect simplifies the task of streaming data between Kafka and external systems, especially NoSQL databases.

In this post, we’ll explore advanced Kafka connectors for integrating with NoSQL databases such as MongoDB, Apache Cassandra, and Elasticsearch. We’ll dive into key configuration strategies, deployment modes, and real-world use cases that enable you to build seamless, real-time data pipelines.

What is Kafka Connect?

Kafka Connect is a framework for connecting Kafka with external data sources and sinks. It supports:

  • Source connectors: Ingest data into Kafka from databases, logs, etc.
  • Sink connectors: Push data from Kafka to external systems

Benefits:

  • Declarative configuration via JSON or REST
  • Distributed and standalone deployment options
  • Built-in offset tracking and error handling
  • Horizontal scalability with minimal code

Why Integrate Kafka with NoSQL Databases?

NoSQL databases are ideal for flexible, scalable, and high-volume storage. Combining them with Kafka enables:

  • Real-time analytics from operational databases
  • Change Data Capture (CDC) for audit and synchronization
  • Denormalized views for fast lookups or search
  • Event sourcing architectures

1. MongoDB Kafka Connector

MongoDB offers an official Kafka Connector that supports:

  • Source connector: MongoDB → Kafka
  • Sink connector: Kafka → MongoDB

Sample Sink Connector Config:

{
"name": "mongo-sink",
"config": {
"connector.class": "com.mongodb.kafka.connect.MongoSinkConnector",
"topics": "user-events",
"connection.uri": "mongodb://user:pass@host:27017",
"database": "analytics",
"collection": "user_events",
"key.converter": "org.apache.kafka.connect.storage.StringConverter",
"value.converter": "org.apache.kafka.connect.json.JsonConverter",
"value.converter.schemas.enable": false
}
}

Supports:

  • Upserts and deletes
  • Schema-less ingestion
  • Change event flattening

Use Cases:

  • Real-time user behavior tracking
  • Updating operational dashboards

2. Cassandra Kafka Connector

The DataStax Kafka Connector allows writing Kafka events to Apache Cassandra.

Sample Sink Config:

{
"name": "cassandra-sink",
"config": {
"connector.class": "com.datastax.oss.kafka.sink.CassandraSinkConnector",
"topics": "orders",
"contactPoints": "cassandra1:9042",
"loadBalancing.localDc": "dc1",
"keyspace": "sales",
"table": "orders",
"mapping": "order_id=value.order_id, amount=value.amount, created_at=value.created_at"
}
}

Features:

  • Batch writes and asynchronous inserts
  • Native support for idempotency
  • High throughput with schema mapping

Use Cases:

  • Storing financial transactions
  • Time-series events for dashboards

3. Elasticsearch Kafka Connector

The Kafka Connect Elasticsearch Sink allows pushing Kafka data into Elasticsearch for full-text search and analytics.

Sample Config:

{
"name": "es-sink",
"config": {
"connector.class": "io.confluent.connect.elasticsearch.ElasticsearchSinkConnector",
"topics": "clickstream",
"connection.url": "http://elasticsearch:9200",
"type.name": "_doc",
"key.ignore": true,
"schema.ignore": true,
"index.name": "clickstream-index"
}
}

Highlights:

  • Automatic index creation
  • Compatibility with Kibana
  • Ideal for log aggregation, search-based analytics

Deployment Options

  1. Standalone Mode
    • Single process for quick testing or simple pipelines
  2. Distributed Mode
    • Recommended for production
    • Handles fault-tolerance, offset checkpointing, and scaling

Use REST API to manage connectors:

POST /connectors
GET /connectors/<name>/status
DELETE /connectors/<name>

Best Practices

  • Schema evolution: Use Avro or JSON with a schema registry
  • Error handling: Configure dead-letter queues or error topics
  • Monitoring: Use JMX, Prometheus, or Control Center for metrics
  • Backpressure management: Tune Kafka Connect workers and connector batching
  • Security: Secure connectors with TLS and SASL for Kafka/NoSQL endpoints

Real-World Use Case: Real-Time Inventory Tracking

  • Source: Inventory events from Kafka
  • Sink 1: MongoDB for user-facing stock dashboard
  • Sink 2: Cassandra for high-speed order reconciliation
  • Sink 3: Elasticsearch for search-based querying in the admin panel

This multi-sink architecture keeps operational and analytical systems in sync.

Conclusion

Kafka Connect provides a powerful abstraction for integrating Kafka with NoSQL databases. Whether you’re syncing data to MongoDB, Cassandra, or Elasticsearch, advanced Kafka connectors make it easy to implement scalable, resilient, and real-time pipelines.

With proper configuration and deployment practices, you can streamline data integration, support mission-critical applications, and build a foundation for modern event-driven architectures.