Are you looking for a real-time streaming solution but confused between Kafka Streams and Apache Spark Streaming? Well, you're not alone. Both of these technologies have been popular among data engineers and stream processing enthusiasts. Which one should you pick? Let's find out.
Kafka Streams vs. Apache Spark Streaming
What are they?
Apache Kafka: a distributed streaming platform that allows you to publish and subscribe to streams of records, similar to a message queue or enterprise messaging system.
Kafka Streams: a client library that enables real-time processing and transforming of data streams on top of Kafka.
Apache Spark: an open-source, distributed computing system used for large-scale data processing and analytics.
Apache Spark Streaming: an extension library of Apache Spark, allowing real-time processing of streaming data.
Features and Differences
| Feature | Kafka Streams | Apache Spark Streaming |
|---|---|---|
| Purpose | Stream processing and transformations | Stream processing and analytics, ML and Big Data |
| Latency | Sub-second latency | 100ms latency or less (in streaming micro-batch) |
| Throughput | Less throughput but scalably distributed | More throughput, auto-tuning of resources |
| Programming Languages | Java and Scala | Java, Scala, Python, R, and SQL |
| Ease of Use | Requires knowledge of Kafka and Streaming KSQL | Requires knowledge of Spark |
| API and Interface | Java DSL, declarative SQL-like queries (KSQL) | Java, Scala, Python, R, SQL, and Graph processing |
| Fault Tolerance, Reliability, and Durability | Automatic fault tolerance, done at partition level | Manual configuration, checkpointing required |
| Maintenance and Learning Curve | Low maintenance, used as a library in Java/Scala | High maintenance, steep learning curve |
Use Cases
Kafka Streams
- Real-time processing of streaming data and data transformations.
- Joining and aggregating data from streams.
- Monitoring and anomaly detection.
- Building microservices and event-driven architectures.
- Real-time machine learning, AI, and predictive analytics.
Apache Spark Streaming
- Large-scale batch processing, streaming, machine learning, graph processing, and SQL queries.
- High-volume data ETL, data transformations, and data lake processing.
- Micro-batching data processing and windowing operations.
- Complex event processing, pattern matching, and fraud detection.
- Interactive data exploration and data visualization.
Conclusion
Choosing the right stream processing engine depends on your use case, workloads, programming proficiency, latency requirements, fault tolerance, and maintainability. If you want low latency and ease of use, Kafka Streams might be an excellent option as it only requires basic knowledge of Kafka. On the other hand, Apache Spark Streaming is a more powerful and feature-rich framework suitable for complex analytics, machine learning, and graph processing. Try out both and see what works best for your needs.
So, what's your pick? Kafka or Spark? Let us know in the comments below.
References
- "Apache Kafka" - https://kafka.apache.org/intro
- "Kafka Streams" - https://kafka.apache.org/documentation/streams/
- "Apache Spark" - https://spark.apache.org/
- "Apache Spark Streaming" - https://spark.apache.org/streaming/
- Kafka vs. Spark Streaming: Which Stream Processing Should You Use? - https://www.altexsoft.com/blog/kafka-vs-spark-streaming-which-stream-processing-should-you-use/