Apache Kafka vs. Flink: The Ultimate Comparison for Data Engineer

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Jan 31
6 min read

In the world of real-time data processing, Apache Kafka and Apache Flink are two of the most popular frameworks that play critical roles. However, while both deal with stream processing, they serve different purposes, and understanding their distinctions is important for choosing the right tool for a given use case. Apache Kafka is a distributed streaming platform that excels at message brokering and log storage, while Apache Flink is a stream processing framework designed to process data at scale, in real-time, with sophisticated stateful computations.

What is Apache Kafka?

Apache Kafka, originally developed by LinkedIn and now maintained by the Apache Software Foundation, is a distributed event streaming platform primarily used for building real-time data pipelines and streaming applications. Kafka can handle massive amounts of high-throughput data, and it is primarily known for its robust message broker capabilities.

Kafka’s key features include:

Publish-Subscribe Model: Kafka allows producers to send messages to topics, which can then be consumed by multiple consumers. This decouples producers from consumers, providing fault tolerance and scalability.
Durability and Reliability: Kafka stores streams of records in a fault-tolerant manner. This means that even in the event of a failure, data can be recovered from Kafka’s distributed log storage.
High Throughput: Kafka is designed to handle large volumes of data with low latency, making it an ideal solution for applications that require real-time data feeds.
Scalability: Kafka is horizontally scalable, meaning more nodes can be added to a Kafka cluster to handle more data or traffic.

Kafka is widely used as a message broker or event store in architectures where a large amount of data needs to be ingested or shared across multiple systems or microservices. However, Kafka itself does not have built-in support for sophisticated data transformations or stateful processing, which brings us to Apache Flink.

What is Apache Flink?

Apache Flink, another project under the Apache Software Foundation, is a powerful framework for distributed stream processing and batch processing. While Kafka is great for message streaming and event storage, Flink is designed to process that data once it is ingested, allowing for sophisticated computations, aggregations, and windowing over large-scale, real-time data streams.

Flink’s key features include:

Stream and Batch Processing: Flink is a unified platform that supports both stream processing and batch processing. Stream processing is particularly useful for real-time analytics, while batch processing is useful for analyzing historical data.
Event Time Processing: Flink allows for event-time processing, meaning it can handle events that arrive out of order or with delays. This is a key feature for processing real-time data, as many systems (e.g., IoT sensors, financial transactions) may have varying latencies.
Stateful Stream Processing: Flink offers built-in support for maintaining state across events. This is crucial for use cases like real-time aggregations, filtering, and complex event processing.
Exactly Once Semantics: Flink ensures exactly-once processing guarantees, meaning that data will not be duplicated or lost, even in the case of failures.
Fault Tolerance: Flink’s stateful nature is accompanied by fault tolerance through checkpointing. It periodically saves the state of the application so that, if the system fails, the application can resume from the last consistent state.

Flink is often used to process the data coming from Kafka (or other data sources) in real-time, enabling use cases like fraud detection, real-time analytics, and event-driven architectures.

Key Differences Between Apache Kafka and Apache Flink

1. Primary Purpose

Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant message delivery, and log storage. Kafka is used primarily for handling data streams, storing events, and enabling the decoupling of producers and consumers.
Apache Flink, on the other hand, is a stream processing framework used to analyze and process data in real time. It excels in performing complex transformations, aggregations, windowing, and stateful computations on the data streams ingested into the system.

2. Functionality and Use Cases

Kafka is used for building data pipelines, event-driven architectures, and messaging systems. It is typically the data bus in an ecosystem, allowing applications to communicate and share data efficiently. Common use cases include log aggregation, real-time data ingestion, and event-driven systems.
Flink is used for processing data streams in real time. It is ideal for applications requiring complex event processing, stateful computations, or real-time analytics. Common use cases include fraud detection in financial systems, anomaly detection, and stream-based ETL (Extract, Transform, Load).

3. Data Storage vs. Data Processing

Kafka acts as a distributed log that stores streams of records for a specified period, allowing consumers to pull messages at their own pace. It guarantees durability and fault tolerance of data but does not directly handle complex data transformations or computations.
Flink is focused on processing the data. It can consume data from Kafka or other sources, process the streams using custom logic, and output the results to sinks such as databases, file systems, or other messaging systems.

4. State Management

Kafka provides a durable and reliable messaging system but does not have built-in support for maintaining state across message streams. It is not designed to perform stateful computations.
Flink, in contrast, is designed for stateful processing. It maintains the state of each stream and provides features like windows, time-based processing, and aggregations that require maintaining state over time.

5. Fault Tolerance

Kafka achieves fault tolerance through replication. Data in Kafka topics is replicated across multiple brokers, ensuring that even if a broker goes down, the data is not lost.
Flink achieves fault tolerance through checkpointing. Flink periodically saves the state of a streaming job so that, in case of failure, the job can be restarted from the last consistent checkpoint, ensuring that the processing is fault-tolerant and consistent.

6. Latency and Throughput

Kafka is designed for high throughput and low latency when it comes to message delivery. It excels in scenarios where the primary requirement is to ingest large volumes of data with minimal delay.
Flink is designed for low-latency stream processing, but the performance depends on the complexity of the computations being performed. It can introduce some latency when processing large or complex datasets, especially when maintaining state or performing windowed computations.

7. Ecosystem Integration

Kafka integrates well with a variety of systems as a messaging backbone. It supports integrations with databases, stream processing frameworks, and monitoring systems.
Flink is often used in conjunction with Kafka, as Flink excels in processing the data streams that Kafka ingests. It can consume data from Kafka topics, process it in real-time, and output the results to other sinks like HDFS, databases, or dashboards.

Feature	Apache Kafka	Apache Flink
Primary Purpose	Distributed event streaming and messaging platform	Stream processing framework for real-time analytics
Functionality	Message brokering, data storage, pub/sub model	Complex event processing, stateful stream processing
Data Storage	Stores streams of records in distributed logs	Does not store data, processes data in real-time
Data Processing	Does not perform complex transformations or stateful processing	Provides real-time processing, windowing, and stateful transformations
State Management	No built-in state management	Built-in stateful stream processing
Fault Tolerance	Data replication for fault tolerance	Checkpointing mechanism for fault tolerance
Throughput and Latency	High throughput and low latency for message delivery	Low-latency processing, but depends on complexity of transformations
Event Time Handling	Does not handle event-time processing	Supports event-time processing and handling of late-arriving data
Exact Processing Semantics	At least once delivery guarantee, configurable to exactly once	Exactly-once processing semantics
Integration	Acts as a message bus or event store, integrates well with other systems	Primarily used for processing data from Kafka and other sources
Use Cases	Event-driven systems, data ingestion, microservices messaging	Real-time analytics, fraud detection, real-time ETL, anomaly detection
Popular Ecosystem	Kafka Streams, Kafka Connect, KSQL	Flink SQL, Flink CEP (Complex Event Processing), Flink’s stateful stream API
Scalability	Horizontally scalable (by adding more brokers)	Horizontally scalable (by adding more TaskManagers)

This table provides a quick reference to the differences between Apache Kafka vs. Flink, highlighting their unique features and the types of use cases they are best suited for.

Conclusion Apache Kafka vs. Flink

While Apache Kafka and Apache Flink both operate within the realm of real-time data, they serve different purposes. Kafka is a robust event streaming platform and message broker, focused on providing reliable, high-throughput message delivery, and persistent storage of streams. It is typically used for ingesting and distributing large amounts of data.

On the other hand, Apache Flink is a powerful stream processing framework designed to perform complex, stateful operations on data streams. Flink excels in real-time analytics, event-time processing, and windowing, making it the ideal choice for applications that need to process and analyze data in motion.

In many architectures, Kafka and Flink are used together: Kafka acts as the backbone for event streaming, while Flink handles the complex processing of that data. Understanding the differences between these two tools helps in choosing the right tool for your streaming and processing needs.