In the world of Big Data, few tools are as ubiquitous — or as frequently confused — as Apache Spark and Apache Kafka.
If you are designing a data architecture, you have likely asked: “Should I use Spark or Kafka?” The answer is rarely one or the other. While they are often mentioned in the same breath, they serve fundamentally different purposes. To put it simply: Kafka moves data; Spark thinks about data.
This guide will break down the differences, strengths, and how they actually work better together.
What is Apache Kafka? (The Messenger)
Think of Apache Kafka as the central nervous system of your data architecture. It is a distributed event streaming platform designed to handle massive amounts of real-time data.
Kafka’s primary job is to ingest streams of data from various sources (like IoT sensors, user clicks, or database changes) and make that data available to other systems safely and instantly. It is built on a Publish-Subscribe (Pub/Sub) model.
- Producers write data to Kafka topics.
- Consumers read data from those topics.
- The Log: Kafka stores these events as a durable “log.” Unlike a traditional message queue that deletes a message once read, Kafka keeps the data for a set period, allowing multiple consumers to read the same data at their own pace.
Best For: Real-time data ingestion, decoupling systems (so they don’t crash each other), and acting as a high-throughput data buffer.
What is Apache Spark? (The Processor)
If Kafka is the nervous system, Apache Spark is the brain. It is a unified analytics engine designed for large-scale data processing.
Spark does not store data long-term (it relies on HDFS, S3, or… Kafka for that). Instead, it pulls data into memory, performs complex computations on it, and then writes the results somewhere else. Spark became famous for being 100x faster than its predecessor, Hadoop MapReduce, because it processes data in RAM (in-memory) rather than constantly writing to a hard drive.
Best For: Complex ETL (Extract, Transform, Load), Machine Learning, SQL analytics on massive datasets, and aggregating historical data.
Head-to-Head: The Key Differences
While there is some overlap (both can “process” streams to an extent), they are optimized for different realities.
The “Versus” Myth: Why You Need Both
In a modern data architecture, you rarely choose between Spark and Kafka. You use them in a pipeline.
Imagine a credit card fraud detection system:
Ingest (Kafka): Millions of swipes happen globally. Kafka captures these transactions instantly as a stream of events. It acts as a buffer, ensuring no data is lost even if the processing layer gets overwhelmed.
Process (Spark): Spark Streaming connects to Kafka. It reads these transactions in real-time, joins them with historical user data (e.g., “Did this user just buy coffee in London after buying gas in New York 5 minutes ago?”), and applies a Machine Learning model to score the fraud probability.
Act: Spark writes the flagged transactions back to a new Kafka topic, which triggers an alert to the fraud team.
Why not just use Spark?
Spark can process data, but it needs a source. If you send millions of records directly to Spark without a buffer, and the Spark cluster crashes, you lose that data. Kafka prevents this data loss.
Why not just use Kafka?
Kafka has a library called Kafka Streams that can do simple filtering and aggregations (like counting clicks). However, if you need to train a Deep Learning model or perform complex SQL queries across terabytes of historical data, Kafka Streams is not powerful enough. You need the heavy lifting of Spark.
Conclusion
The choice comes down to what problem you are solving right now:
Choose Kafka if: You need to move data from Point A to Point B reliably, decouple your services, or buffer a massive influx of events.
Choose Spark if: You need to slice and dice that data, train machine learning models, or run complex SQL queries on data at rest or in motion.
For most Data Engineers, the answer isn’t “Spark vs. Kafka.” It’s Spark + Kafka. Together, they form the backbone of the modern, real-time data pipeline.
Top comments (0)