Apache Kafka is a distributed, fault-tolerant, high-throughput event-streaming platform. LinkedIn originally developed it to handle massive data pipelines. The Apache Software Foundation now maintains this open-source project.

The Commit Log Mental Model

Kafka implements a distributed commit log at its core. Think of an append-only logbook where entries can't change. This immutable design enables powerful capabilities.

The commit log provides several advantages:

• Consumers can replay events from any point in time
• Multiple applications independently read the same event stream
• Systems resume exactly where they left off after failures
• Complete event history remains available for compliance and debugging

Kafka can scale production clusters up to a thousand brokers. It processes trillions of messages per day and petabytes of data. The platform delivers messages at network limited throughput using a cluster of machines. Latencies can be as low as 2ms.

Modern organizations leverage Kafka for diverse scenarios. Let's explore the primary applications.

Real-Time Analytics
Organizations stream clickstream data directly to analytics engines. IoT platforms process sensor readings for immediate insights. Teams monitor application metrics and system health continuously.

Microservices Communication
Kafka decouples services with event-driven architecture patterns. Teams implement Event Sourcing for complete audit trails. Command Query Responsibility Segregation (CQRS) systems become straightforward.

Log Aggregation
Companies centralize logs from thousands of servers efficiently. Processing and routing happen based on log content. Real-time analysis and alerting respond to critical events.

Change Data Capture (CDC)
Database changes stream in real-time to downstream systems. Multiple applications stay synchronized automatically. Event-driven pipelines replace batch processing.

IoT Data Ingestion
Kafka handles substantial device event volumes. Buffer and processing capabilities manage unpredictable data bursts. Real-time monitoring enables immediate device control.

The key differentiator? Kafka combines messaging flexibility with database durability. Messages persist according to retention policies, unlike traditional queues. This unique approach enables replayable, durable event streams.

Understanding Kafka requires mastering several interconnected concepts. Let's break them down clearly.

Topics, Partitions, and Offsets

Topics organize events into named categories or feeds. Each topic describes its event type clearly. Examples include payment-transactions, user-signups, or inventory-updates. Producers publish to specific topics. Consumers subscribe to topics they need.

Partitions enable Kafka's massive scalability. Topics split into one or more partitions automatically. Each partition maintains an ordered, immutable record sequence.

Here's why partitions matter:

• Multiple consumers process different partitions simultaneously
• Partitions distribute across multiple brokers
• Kafka guarantees order within single partitions only

Offsets are sequential integers identifying each partition record. Think of them as line numbers. Offsets start at zero and increment monotonically. Consumers track their position using offsets.

This mechanism enables powerful features:

• Resume processing after failures
• Replay data from specific points
• Process at independent speeds

Producers and Consumers

Producers write events to Kafka topics. They connect to clusters and serialize data. Producers send messages to appropriate topics.

Key producer capabilities include:

• Specify partition keys for controlled routing
• Use random distribution for load balancing
• Implement custom partitioning logic

Producers support different reliability levels. Fire-and-forget offers maximum speed. Synchronous sends wait for acknowledgment. Asynchronous sends balance performance and reliability.

Consumers read events from Kafka topics. They maintain position using offsets internally. Kafka stores these offsets for recovery.

Consumer Groups enable scalable consumption patterns:

• Group members share the same group ID
• Each partition assigns to one group consumer
• Multiple groups consume topics independently
• Automatic rebalancing handles membership changes

Offset Management provides consumption control:

• Automatic commits save position periodically
• Manual commits give explicit acknowledgment
• Seek operations enable replay scenarios

Brokers and Replication

Brokers are Kafka servers storing and serving data. Each broker handles several responsibilities:

• Store partition replica data
• Process client produce/consume requests
• Manage replication for durability

The Leader-Follower Model ensures high availability. Each partition has one leader broker. Followers replicate the leader's data continuously. Failed leaders trigger automatic follower promotion.

In-Sync Replicas (ISR) maintain consistency across brokers. ISR includes replicas fully synchronized with leaders. Only ISR members qualify for leadership. Producers can await ISR acknowledgment for durability.

Replication Factor determines your fault tolerance level. Many production deployments commonly use a replication factor of three. This tolerates two broker failures without data loss. Higher replication increases durability but requires more resources. The optimal replication factor depends on your specific reliability requirements and resource constraints.

Apache Kafka 4.0 was released on March 18, 2025. It represents a significant milestone in the platform's evolution (Apache Kafka 4.0: KRaft, New Features, and Migration) (Apache Kafka 4.0: Features & Changes). The complete removal of ZooKeeper dependency headlines this release. KRaft (Kafka Raft) becomes the exclusive metadata management solution.

The End of the ZooKeeper Era

For over a decade, Kafka required ZooKeeper for coordination. This dependency added significant operational complexity. Teams managed two distributed systems simultaneously. Different security models increased maintenance burden. Separate monitoring stacks doubled observability efforts. Additional failure points complicated troubleshooting.

ZooKeeper served Kafka well for years. The Apache Kafka community expresses gratitude to the ZooKeeper community. ZooKeeper was the backbone of Kafka for more than 10 years. Kafka would most likely not be what it is today without it.

KRaft Architecture Benefits

KRaft integrates metadata management directly into Kafka. Controller nodes form quorums using Raft consensus. These controllers can also serve as data brokers.

The benefits are transformative:

Single System Management

• No ZooKeeper configurations or deployments
• Unified monitoring and logging infrastructure
• Consistent security model throughout

Improved Scalability

• Enhanced support for large partition counts per cluster
• Faster metadata operations reduce latency
• Enhanced performance for administrative tasks

Controller Efficiency

• Significantly improved failover times for large partition counts
• Faster recovery in various scenarios
• Improved cluster availability metrics

Simplified Security

• Single authentication and authorization model
• Streamlined SSL/TLS configuration
• Enhanced audit logging capabilities

Kafka's ecosystem extends beyond basic messaging. Rich APIs enable sophisticated streaming applications.

Core APIs

Producer API
Publishers send data streams to topics. Features include:

• Asynchronous and synchronous modes
• Automatic batching improves throughput
• Configurable partitioning strategies
• Built-in retry with backoff
• Compression support (snappy, lz4, gzip, zstd)

Consumer API
Applications read topic data streams. Capabilities include:

• Consumer group coordination
• Automatic offset management
• Flexible partition assignment
• Seek and replay functionality
• Controlled polling interface

Admin API
Programmatic cluster management provides:

• Topic creation and deletion
• ACL (Access Control List) management
• Metadata and configuration inspection
• Partition reassignment control
• Consumer group monitoring

Kafka Streams

This powerful library builds streaming applications directly.

Processing Capabilities:

• Stateful operations with local stores
• Windowing (tumbling, hopping, sliding, session)
• Stream-stream and stream-table joins
• Aggregations and transformations
• Exactly-once processing guarantees

Kafka Connect

Connect simplifies external system integration.

Source Connectors import data:

• Database CDC (Debezium)
• File system connectors
• Cloud storage (S3, GCS, Azure)
• Message queues (JMS, RabbitMQ)
• SaaS applications (Salesforce, ServiceNow)

Sink Connectors export data:

• JDBC database sinks
• Elasticsearch connector
• Cloud storage sinks
• HTTP sink connector
• Analytics platforms

Ecosystem Tools

Monitoring Solutions:

Kafdrop offers a lightweight web UI. View topics, partitions, and messages easily. Monitor consumer groups and lag. Browse and search messages.

Conduktor provides comprehensive management. Visual topic exploration simplifies navigation. Schema registry management included. Security and ACL control integrated.

Cruise Control automates cluster rebalancing. Anomaly detection identifies issues early. Resource optimization reduces Kafka costs.

Schema Registry manages data contracts:

• Centralized schema storage
• Evolution and compatibility checking
• Avro, Protobuf, JSON Schema support
• Client and Connect integration

MirrorMaker 2 enables multi-datacenter replication:

• Cross-region data synchronization
• Active-active and active-passive modes
• Topic filtering and renaming
• Offset translation for recovery

Understanding Kafka's sweet spot prevents architectural mistakes. Let's explore ideal scenarios and alternatives.

High-Volume Event Streaming

Kafka excels processing substantial daily event volumes. Kafka delivers high throughput, near-saturating the disk I/O available (Apache Kafka® Performance, Latency, Throughout, and Test Results). Each byte produced writes just once onto disk on an optimized code path.

Use it for:

• Real-time analytics pipelines
• IoT sensor data ingestion
• Financial market data feeds
• Microservice log aggregation

Event-Driven Architectures

Build reactive systems with Kafka:

• Order processing triggering downstream services
• Real-time fraud detection systems
• User activity personalization
• Multi-channel notification fanout

Data Integration and Stream Processing

Kafka serves as a central data infrastructure. Use it for database change capture (CDC) and cross-system synchronization. It excels at streaming data lake ingestion and producer-consumer decoupling. Transform data in real-time with windowed aggregations and stream enrichment. Analyze user sessions and process complex events

The fundamental way Kong and Kafka integrate is by treating Kafka event streams as first-class APIs. Instead of treating Kafka as a separate, opaque backend system that requires specialized clients, Kong allows engineers to manage, secure, and expose Kafka topics just like they would a REST or GraphQL API.

This is achieved primarily through the Kong Event Gateway, which sits in front of your Kafka brokers.

Event Gateway + Kafka in Action

1. Protocol Mediation & Abstraction

• The Problem: Consuming Kafka natively requires specific Kafka clients and libraries, which can be a barrier for web clients, external partners, or internal teams unfamiliar with the protocol.
• The Solution: Kong acts as a bridge. It can expose Kafka topics as consumer-friendly protocols like WebSockets or HTTP. This allows developers to consume streaming data using standard web technologies without needing a native Kafka client.

2. Unified Security & Governance

• The Problem: Securing Kafka often requires different mechanisms (like SASL/Kerberos) than standard web APIs (like OAuth/OIDC), creating fragmented security policies.
• The Solution: Engineers can apply the same security policies to Kafka streams that they use for REST APIs. You can enforce standard authentication (OAuth2, JWT, OIDC) at the gateway level before a request ever reaches the Kafka broker.

3. Message-Level Traffic Shaping

• The Problem: Protecting downstream services or clients from being overwhelmed by high-volume topics can be difficult.
• The Solution: Kong allows you to apply traffic control logic at the message level. This includes rate limiting and traffic shaping, ensuring reliable consumption without overloading consumers.

4. Developer Experience & Discovery

• The Problem: Kafka topics are often undocumented or hard to discover, leading to "tribal knowledge" silos about which topics contain what data.
• The Solution: You can publish Kafka streams to the Kong Developer Portal. This allows developers to browse, read documentation, and self-service access to data streams alongside standard REST services, effectively creating an "API Marketplace" for your real-time data.

5. Unified Observability

• The Problem: Monitoring API traffic and Kafka throughput usually requires separate tools.
• The Solution: Kong provides a single pane of glass for monitoring. Engineers can measure and observe consumption across both standard APIs and Kafka event streams in one unified dashboard.

For an engineer, this integration transforms Kafka from a raw backend storage mechanism into a productized service. It decouples the complexity of the Kafka cluster from the consumers, allowing you to securely expose real-time data to mobile apps, web browsers, and third-party partners using standard API management practices

What's the difference between Kafka and traditional message queues?

Traditional message queues delete messages after consumption. Kafka persists messages according to retention policies. This enables replay and multiple independent consumers. Kafka also provides higher throughput and horizontal scalability.

How much data can Kafka handle?

Kafka scales to trillions of messages per day and petabytes of data. Production clusters can run thousands of brokers with hundreds of thousands of partitions. The actual limit depends on your hardware and network capacity.

Is Kafka suitable for small projects?

Kafka works best for high-volume scenarios. Small projects might benefit from simpler solutions like RabbitMQ or cloud-managed services. Consider Kafka when you need replay capability, multiple consumers, or expect significant growth.

What's the learning curve like?

Basic producer-consumer patterns take days to learn. Understanding partitions and consumer groups requires weeks. Production-ready deployments need months of experience. The ecosystem tools add complexity but provide powerful capabilities.

Can Kafka guarantee message ordering?

Kafka guarantees order within a single partition. Use partition keys to route related messages to the same partition. Global ordering across partitions isn't guaranteed and would eliminate parallelism benefits.

How does Kafka compare to cloud streaming services?

Kafka provides full control and cost efficiency at scale. Cloud services like AWS Kinesis or Azure Event Hubs reduce operational overhead. Choose based on your team's expertise and operational requirements.