Key Differences Between Apache Kafka and Confluent Kafka

Apache Kafka, a widely adopted open-source distributed event-streaming platform, powers real-time data pipelines and applications. Confluent Kafka builds on Apache Kafka by offering a commercial, enterprise-grade distribution packed with additional features and tools for a seamless developer and operational experience. Understanding the differences between the two is crucial for choosing the right platform for your organization’s needs. In this blog, we’ll explore the key differences between Apache Kafka and Confluent Kafka, with examples and insights.

---

1. Core Offering vs. Enterprise Features

Apache Kafka

Apache Kafka is an open-source project governed by the Apache Software Foundation. It provides core event streaming capabilities, including:

• Producers and Consumers for message production and consumption.
• Topics for organizing messages.
• Brokers for distributed storage.
• Basic configuration for replication, retention, and partitioning.

While it’s powerful, Apache Kafka lacks many advanced features needed for enterprise use cases.

Confluent Kafka

It includes all the features of Apache Kafka and extends them with enterprise-specific tools and services:

• Schema Registry: Enforces schemas for message consistency.
• ksqlDB: SQL-based stream processing.
• Kafka Connect: Pre-built connectors for integrating data with external systems.
• Control Center: A GUI for monitoring and managing clusters.
• Enhanced Security: LDAP integration, Role-Based Access Control (RBAC), and audit logging.
• Cluster Linking: Simplifies replication between Kafka clusters.

Example:

# Apache Kafka does not include a Schema Registry.
kafka-console-producer --broker-list localhost:9092 --topic test

# Confluent Kafka with Schema Registry:
kafka-avro-console-producer --broker-list localhost:9092 \
  --topic test --property schema.registry.url=http://localhost:8081 \
  --property value.schema='{"type":"record","name":"test","fields":[{"name":"field1","type":"string"}]}'

---

2. Deployment and Management

Apache Kafka

Setting up and managing Apache Kafka clusters requires manual intervention and expertise. You need to configure Zookeeper, brokers, and clients separately, often relying on custom scripts or third-party tools for cluster monitoring and scaling.

Confluent Kafka

Confluent simplifies deployment with pre-built installation packages, Helm charts for Kubernetes, and managed cloud services (Confluent Cloud). It also offers:

• Automated broker scaling.
• Built-in monitoring through Control Center.
• Simplified disaster recovery with cluster linking.

Example Deployment with Helm:

# Apache Kafka Setup:
kubectl apply -f kafka-deployment.yaml

# Confluent Kafka Setup:
helm repo add confluentinc https://packages.confluent.io/helm
helm install confluent-kafka confluentinc/cp-helm-charts

---

3. Security

Apache Kafka

Basic security features are available, including:

• SSL/TLS encryption.
• Simple authentication using SASL/PLAIN.
• Access control using ACLs.

However, managing these features at scale can be cumbersome.

Confluent Kafka

Confluent Kafka offers advanced security features:

• RBAC: Fine-grained access control for topics, consumers, and producers.
• Audit Logs: Tracks access and changes for compliance.
• Centralized Security Management: Simplifies configuration across clusters.

Example RBAC Setup:

# Apache Kafka ACLs:
kafka-acls --add --allow-principal User:test_user --operation Write --topic test

# Confluent Kafka RBAC:
confluent iam rolebinding create --principal User:test_user --role ResourceOwner --resource Topic:test

---

4. Stream Processing

Apache Kafka

Stream processing in Apache Kafka requires integrating external libraries like Kafka Streams or Apache Flink. This approach requires developers to write Java or Scala code, which can be complex for non-programmers.

Confluent Kafka

Confluent provides ksqlDB, a SQL-based engine for stream processing. This makes it easier to query, transform, and process data in real time without writing code.

Example:

-- Stream processing with ksqlDB
CREATE STREAM pageviews (user_id VARCHAR, page_id VARCHAR) WITH (KAFKA_TOPIC='pageviews', VALUE_FORMAT='JSON');
CREATE TABLE user_counts AS SELECT user_id, COUNT(*) FROM pageviews GROUP BY user_id;

---

5. Integration

Apache Kafka

Apache Kafka integrates with other systems through Kafka Connect, but you’ll need to find or build suitable connectors.

Confluent Kafka

It includes over 120 pre-built connectors for systems like:

• Databases: MySQL, PostgreSQL, Oracle.
• Cloud Storage: S3, Azure Blob, Google Cloud Storage.
• Analytics: Elasticsearch, Snowflake.

Example Kafka Connect Configuration:

{
  "name": "jdbc-source",
  "config": {
    "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
    "tasks.max": "1",
    "connection.url": "jdbc:mysql://localhost:3306/mydb",
    "topic.prefix": "mysql-",
    "mode": "incrementing",
    "incrementing.column.name": "id"
  }
}

---

6. Licensing and Cost

Apache Kafka

Apache Kafka is free to use under the Apache 2.0 license, with no cost for software but additional costs for infrastructure and maintenance.

Confluent Kafka

This offers a Community Edition (free) and a paid Enterprise Edition with advanced features and support. Confluent Cloud, a fully managed service, comes with usage-based pricing.

Key Consideration:

Organizations needing advanced features, simplified management, and enterprise-grade support often choose Confluent Kafka despite the additional cost.

---

7. Managed Cloud Service

Apache Kafka

Deploying Apache Kafka on the cloud requires manual setup or third-party managed services.

Confluent Kafka

Confluent Cloud provides a fully managed Kafka experience, supporting multi-cloud and hybrid environments with SLA-backed uptime guarantees.

---

Conclusion

Apache Kafka and Confluent Kafka cater to different user needs. Apache Kafka is ideal for small teams or organizations that need a cost-effective, customizable solution. Confluent Kafka, with its extended features and enterprise-grade tools, is better suited for large-scale deployments and teams requiring advanced capabilities.

Evaluate your requirements, budget, and scalability needs to choose the platform that fits your use case best. That’s it for now. I hope this article gave you some useful insights on the topic. Please feel free to drop a comment, question or suggestion.