An Introduction to Stream Processing with Apache Kafka and Flink
Quick Summary (TL;DR)
Stream processing is the practice of processing data in real-time as it is generated. This contrasts with batch processing, which operates on large, static datasets. A modern stream processing architecture typically consists of two key components: a distributed log like Apache Kafka, which acts as a durable, high-throughput message bus for real-time data streams; and a stream processing engine like Apache Flink or Spark Streaming, which provides the computational framework to run transformations, aggregations, and other logic on the continuous stream of data.
Key Takeaways
- Batch vs. Stream: Batch processing works on data at rest (e.g., running a job on all of yesterday’s data). Stream processing works on data in motion (e.g., processing every single event as it happens).
- Kafka is the Nervous System: Apache Kafka has become the de-facto standard for the real-time data backbone. It is a distributed event streaming platform that allows services to publish and subscribe to streams of events reliably and at massive scale.
- Flink is for Stateful Computation: Apache Flink is a powerful, open-source stream processing framework. Its key feature is its ability to perform complex, stateful computations over unbounded streams of data with low latency.
The Solution: From Batch to Real-Time
Many business problems require immediate insights from data. You can’t wait for a nightly batch job to tell you that a fraudulent transaction is happening right now. Stream processing provides the solution by allowing you to build applications that react to data as it is created. This enables a wide range of real-time use cases, from live dashboards and anomaly detection to real-time recommendations and dynamic pricing. The combination of Kafka (for data transport) and Flink (for computation) provides a scalable and fault-tolerant foundation for building these sophisticated, real-time applications.
A Typical Streaming Architecture
Data Ingestion: Data from various sources (e.g., user clicks on a website, IoT sensor readings, database changes) is published as events to a topic in an Apache Kafka cluster.
Stream Processing: An Apache Flink application subscribes to one or more Kafka topics. The Flink job runs continuously, consuming events from Kafka as they arrive.
Stateful Computation: The Flink application performs a stateful operation. For example, it might maintain a running count of user activity over a 5-minute sliding window, or join two different event streams together.
Output (Sinks): The results of the Flink computation are then sent to an external system, called a sink. This could be another Kafka topic, a real-time dashboard, a database, or an alerting system.
The Benefits of Managed Cloud Services
Running a distributed system like Kafka and Flink is operationally complex. Managed cloud services make this much easier:
- Managed Kafka: Services like Amazon MSK or Confluent Cloud provide a fully managed Kafka service, handling the setup, scaling, and maintenance of the Kafka cluster.
- Managed Flink: Services like Amazon Kinesis Data Analytics or Ververica Platform provide a serverless or managed environment for deploying and scaling your Flink applications.
Common Questions
Q: What does “stateful” stream processing mean? Stateful processing means the application maintains some memory or context over time. For example, to count the number of clicks in the last minute, the application needs to store the state of the count. Flink has excellent, built-in support for managing this state in a fault-tolerant way.
Q: What is the difference between Flink and Spark Streaming? Both are powerful stream processing engines. Flink was designed from the ground up as a true, one-event-at-a-time stream processor, which can lead to lower latency. Spark Structured Streaming uses a “micro-batch” approach, where it processes data in very small, frequent batches. While both are excellent tools, Flink is often favored for use cases requiring very low latency and complex state management.
Q: When should I use stream processing instead of batch processing? Use stream processing when your use case requires low-latency results and you need to react to events as they happen. Use batch processing when you are dealing with large volumes of data where real-time results are not necessary and it’s more efficient to process the data periodically.
Tools & Resources
- Apache Kafka: The official website for the leading distributed event streaming platform.
- Apache Flink: The official website for the open-source, unified stream-processing and batch-processing framework.
- Confluent Cloud: A fully-managed, cloud-native Kafka service from the original creators of Kafka.
- Ververica Platform: An enterprise-grade stream processing platform, built by the original creators of Apache Flink.
Related Topics
Stream Processing & Real-time Architecture
Data Pipeline Architecture
- What is Data Engineering? A Guide to Building Data Pipelines
- Modern Data Pipeline Architecture
- Data Orchestration with Airflow and Dagster
- A Guide to Data Pipeline Orchestration with Apache Airflow
Data Processing & Technologies
Data Governance & Quality
Data Storage & Architecture
Need Help With Implementation?
Building real-time data applications requires a specialized skill set in distributed systems and stream processing. Built By Dakic provides expert consulting on real-time data platforms, helping you design and build scalable and resilient streaming architectures using best-in-class tools like Kafka and Flink. Get in touch for a free consultation.