Major Differences Between Apache Kafka and Apache Flink

Apache Kafka and Apache Flink are both used in real-time data processing, but they serve different purposes in the data pipeline.

| Feature | Apache Kafka | Apache Flink |
|---------|---------------|--------------|
| Purpose | Distributed message broker and event streaming platform. | Stream processing engine for real-time analytics. |
| Core Functionality | Stores and transports events/messages between producers and consumers. | Processes, transforms, and analyzes data streams in real-time. |
| Data Handling | Durable, persistent log storage (message replay, event sourcing). | In-memory stream processing (real-time analytics, transformations). |
| Processing Model | Message queuing and event streaming (Pub/Sub). | Stateful stream processing (low-latency computation). |
| Fault Tolerance | Stores messages for a configurable retention period (default 7 days). | Checkpoints and exactly-once stateful processing. |
| Latency | Low (milliseconds to seconds, depending on configuration). | Ultra-low (milliseconds). |
| Scalability | Scales horizontally using partitions. | Scales dynamically using parallel execution. |
| Event Time Processing | Basic event-time support via Kafka Streams. | Advanced event-time support (watermarks, late arrival handling). |
| State Management | Kafka Streams provides local state stores. | Built-in stateful processing with persistent backends like RocksDB. |
| Data Replay | Yes, events are retained and replayed. | No, unless combined with Kafka or another storage system. |
| Batch Processing Support | Not designed for batch processing. | Supports batch as a special case of streaming. |
| SQL Support | Supports ksqlDB (limited SQL-based stream processing). | Supports Flink SQL/Table API (more powerful than ksqlDB). |
| Integration | Works with Flink, Spark, Hadoop, Elasticsearch, databases, cloud storage. | Works with Kafka, Hadoop, AWS S3, relational databases, and more. |

When to Use Kafka vs Flink

✅ When to Use Apache Kafka

As a message broker to transport real-time events between systems.
For event sourcing where events must be stored, replayed, and persisted.
For decoupling microservices, allowing asynchronous communication.
For log aggregation and audit trail (e.g., logging systems, monitoring data).
For streaming ETL pipelines, ingesting data from multiple sources and forwarding it to databases or processing engines.
For event-driven architectures, where producers publish events and multiple consumers process them.

❌ When Not to Use Kafka

For real-time analytics and stream processing (Kafka alone doesn’t process data; it transports it).
For complex event-time windowing (Kafka Streams is limited compared to Flink).
When stateful transformations are required (Kafka Streams supports this but is less powerful than Flink).

✅ When to Use Apache Flink

For real-time analytics, such as fraud detection, anomaly detection, and live dashboards.
For event-time processing, handling late-arriving data with watermarks.
For complex streaming transformations, such as windowing, joins, aggregations, and stateful computations.
For machine learning on streaming data, such as real-time recommendations.
For stream-based ETL, where data needs filtering, deduplication, and enrichment before reaching the destination.

❌ When Not to Use Flink

As a message broker or event store (Flink does not store events; it processes them).
For simple data transport (Kafka is a better fit for message passing).
For high-throughput durable storage (Flink works best when paired with Kafka for data ingestion).

Kafka and Flink Together

Many real-world architectures use both Kafka and Flink together:

Kafka acts as the event store and message broker.
Flink consumes data from Kafka, processes it, and sends results to databases, dashboards, or other services.

Example Use Case

🚀 Fraud Detection System

Kafka collects and stores transactions from a banking system.
Flink continuously processes the transactions, checking for fraud patterns.
If fraud is detected, Flink sends alerts to a database or notification system.
Kafka stores all transactions so that past events can be analyzed later.

Final Thought

Use Kafka for storing, streaming, and transporting data.
Use Flink for processing, analyzing, and transforming data in real-time.
Best practice: Combine Kafka + Flink for an end-to-end real-time data processing pipeline. 🚀