What is Apache Kafka?

Definition of Apache Kafka

Apache Kafka is a distributed data streaming platform that enables publishing, subscribing, storing, and processing streams of records in real-time. Originally created by LinkedIn in 2011 and donated to the Apache Software Foundation, Kafka has become the de facto standard for event-driven architectures and systems requiring reliable data transmission at scale. Kafka combines message queue functionality with persistent storage and stream processing capabilities. Used by over 80% of Fortune 100 companies, Kafka processes trillions of messages daily worldwide and forms the backbone of modern data infrastructure across industries.

Architecture and Key Kafka Concepts

Kafka’s architecture is built on several fundamental concepts that together create a robust, horizontally scalable system:

• Topics: Named categories or channels to which messages are published. Each topic is divided into partitions that enable parallel processing and horizontal scaling. Partitions are the unit of parallelism in Kafka.
• Producers: Applications that publish messages to topics. Producers can be configured with different reliability levels (acks=0, 1, all) and partitioning strategies (round-robin, key-based, custom).
• Consumers: Applications that read messages from topics. Consumers are organized into consumer groups that provide automatic load balancing and fault tolerance. Each partition is assigned to exactly one consumer within a group.
• Brokers: Kafka servers that store data and serve clients. A cluster consists of multiple brokers for high availability. Each partition has a leader broker handling reads and writes, with follower brokers maintaining replicas.
• KRaft (Kafka Raft): The new consensus mechanism replacing ZooKeeper for cluster metadata management. KRaft simplifies Kafka deployment by eliminating the ZooKeeper dependency and is production-ready from Kafka 3.5 onward.

Data Flow and Delivery Guarantees

Kafka provides configurable delivery guarantees:

• At-most-once: Messages may be lost but are never duplicated (fastest, lowest overhead)
• At-least-once: Messages are never lost but may be duplicated (default behavior)
• Exactly-once: Through idempotent producers and transactional messaging, Kafka achieves exactly-once semantics across producers and consumers

Data retention in Kafka is configurable by time (e.g., 7 days) or size (e.g., 100 GB). Unlike traditional message queues, messages are not deleted after being read, allowing multiple consumer groups to independently process the same data stream at their own pace.

Kafka in Event-Driven Architecture

Apache Kafka serves as the foundation for modern event-driven architectures (EDA). Unlike traditional request-response communication, EDA is based on asynchronous exchange of events between loosely coupled components.

Architecture Patterns with Kafka

• Event Sourcing: Kafka serves as an immutable event log representing the source of truth about system state. Every state change is stored as an event, enabling complete audit trails and the ability to replay history.
• CQRS (Command Query Responsibility Segregation): Kafka synchronizes separate read and write models, with commands distributed via topics and read-optimized materialized views maintained independently.
• Saga Pattern: Distributed transactions are implemented through event coordination across multiple microservices, with Kafka ensuring reliable delivery and ordering.
• Outbox Pattern: Changes and events are stored in a single database transaction, then a connector reliably publishes them to Kafka, ensuring consistency between database state and published events.
• Change Data Capture (CDC): Database changes are captured as events into Kafka topics, enabling real-time synchronization between systems without modifying application code.

Kafka Streams and Stream Processing

Kafka offers integrated stream processing capabilities through multiple technologies:

Kafka Streams

Kafka Streams is a lightweight Java library for building stream processing applications that read from and write to Kafka. Key characteristics include:

• No separate compute cluster required — runs as a regular Java application
• Stateful operations: aggregations, joins, windowing with automatic state management via RocksDB
• Built-in fault tolerance through changelog topics and automatic state restoration
• Elastic scaling by simply adding or removing application instances
• Interactive Queries for accessing local state stores from external applications

ksqlDB

ksqlDB enables stream processing using familiar SQL syntax:

• Creation of real-time materialized views over streaming data
• Streaming ETL without writing application code
• Push queries (continuous) and pull queries (point-in-time) for different use cases
• Native integration with Kafka Connect for source and sink connectivity

Integration with External Frameworks

For more complex processing requirements, Kafka integrates seamlessly with Apache Flink (stateful stream processing with low latency and advanced windowing), Spark Structured Streaming (unified batch-and-stream processing), and Apache Beam (portable processing pipelines across multiple execution engines).

Kafka Connect and Integration Platform

Kafka Connect is the framework for scalable, reliable integration between Kafka and external systems:

• Source Connectors: Capture data from source systems (databases, file systems, APIs, SaaS applications) and write to Kafka topics
• Sink Connectors: Read data from Kafka topics and write to target systems (Elasticsearch, HDFS, S3, data warehouses)
• Over 200 pre-built connectors available in the Confluent Hub
• Automatic schema management through Schema Registry (Avro, Protobuf, JSON Schema)
• Horizontal scaling through distributed connector tasks across multiple workers

Popular connectors include Debezium (CDC for relational databases), JDBC Source/Sink, Elasticsearch Sink, S3 Sink, MongoDB Source/Sink, and Snowflake Sink.

Apache Kafka Use Cases

Kafka finds application across a wide range of scenarios:

Use Case	Description	Typical Throughput
System Integration	Connecting heterogeneous systems via Kafka Connect	10K-100K msgs/s
Log Aggregation	Centralizing logs from hundreds of services	100K-1M msgs/s
Real-time Analytics	Processing clickstreams, transactions, and metrics	1M+ msgs/s
IoT Data Processing	Ingesting and processing device events	10M+ msgs/s
Microservices Communication	Asynchronous service-to-service messaging	10K-500K msgs/s
Data Replication	Cross-region and cross-datacenter synchronization	variable
Event Sourcing	Persisting business events as source of truth	variable
Fraud Detection	Real-time analysis of transactions for suspicious patterns	100K+ msgs/s

Operating Kafka: Best Practices

Cluster Sizing

Proper Kafka cluster sizing depends on several factors:

• Throughput: Expected messages per second and average message size
• Retention: How long data must be stored (affects disk requirements)
• Replication factor: Typically 3 for production environments
• Partition count: Determines maximum parallelism (generally 10-30 partitions per topic for most workloads)

Monitoring and Operations

Critical metrics for Kafka cluster health:

• Under-replicated Partitions: Indicates replication issues requiring immediate attention
• Consumer Lag: Delay between message production and consumption — growing lag signals processing bottlenecks
• Request Latency: Broker response times for produce/fetch requests (p99 latency is key)
• Disk Usage: Storage consumption per broker and partition growth rates
• Network Throughput: Bytes in/out per broker to detect saturation

Tools such as Confluent Control Center, Kafdrop, AKHQ, and Prometheus/Grafana dashboards enable effective Kafka monitoring.

Security

Kafka provides comprehensive security features:

• Encryption: TLS/SSL for data in transit between clients and brokers
• Authentication: SASL mechanisms (PLAIN, SCRAM-SHA-256/512, GSSAPI/Kerberos, OAuthBearer)
• Authorization: ACLs for fine-grained access control at topic and consumer group level
• Schema Registry: Message format validation to prevent incompatible changes from breaking consumers

Kafka in the Cloud

Major managed Kafka services reduce operational burden significantly:

• Confluent Cloud: Fully managed Kafka service from the Kafka founders with enterprise features (Stream Governance, cluster linking)
• Amazon MSK: Managed Streaming for Apache Kafka on AWS with native IAM integration
• Azure Event Hubs: Kafka-compatible streaming platform on Azure with built-in AMQP support
• Google Cloud Pub/Sub: Alternative messaging service with Kafka bridge for compatibility
• Aiven for Apache Kafka: Multi-cloud managed Kafka with strong open-source commitment

Managed services are ideal for organizations wanting to focus on application logic rather than infrastructure management, though self-hosted deployments offer more control and may be more cost-effective at very high volumes.

Business Applications and Strategic Value

Implementing Apache Kafka brings strategic benefits to organizations:

• Real-time processing: Immediate response to business events — from fraud detection in milliseconds to real-time personalization of customer experiences
• Horizontal scalability: Processing millions of events per second while maintaining low latency by adding brokers and partitions
• Fault tolerance: Replication across multiple brokers and data centers ensures business continuity even during hardware failures
• Loose coupling: Independent development and deployment of services accelerates time-to-market and reduces deployment risk
• Data democratization: A central event backbone makes data accessible to all departments and applications, breaking down data silos

ARDURA Consulting supports organizations in acquiring specialists with Apache Kafka and event-driven architecture experience who can design, implement, and operate scalable streaming solutions. From architecture consulting to hands-on implementation, our experts provide the expertise needed for successful Kafka projects.

Summary

Apache Kafka has revolutionized the way organizations process and transmit data, becoming a key component of modern IT architectures. From system integration through real-time analytics to event-driven microservices, Kafka enables building responsive, scalable, and fault-tolerant applications. With its rich ecosystem of Kafka Streams, Connect, and ksqlDB, it provides a complete streaming platform for diverse requirements. ARDURA Consulting offers access to Kafka experts who help with designing, implementing, and optimizing streaming platforms, guiding organizations on their journey to event-driven architecture.