Kafka Streaming vs Spark Streaming

Kafka Streaming and Spark streaming are distributed computing frameworks that allow the processing of real-time data streams. In this article, you will see some differences between Kafka Streaming vs. Spark Streaming.

What is Data Streaming?

Data Streaming is a method in which input is produced continuously to perform transformations. The output is also retrieved as a constant data stream, also called setting data in motion.

What is Kafka Stream?

Kafka Streams is a library for building streaming applications that transform input Kafka topics into output Kafka topics. Kafka Streams (Kstreams) internally uses producer and consumer libraries. It is coupled with Kafka, and the API allows you to leverage the abilities of Kafka by achieving Data Parallelism, Fault-tolerance, low latency, and much more.

What is Spark Stream?

Spark stream is an extension of the core Spark API that provides scalable, high-throughput, fault-tolerant stream processing of live data streams. It allows real-time data processing from various sources like Kafka topics, Flume, Amazon Kinesis, etc. The processed data can be sink to file systems, databases, live dashboards, etc. 

This article describes the difference between streaming part of Spark vs Kafka.

Key difference between Kafka streaming and Spark streaming

	Kafka Streaming	Spark Streaming
Technology stack	Kafka Streams is a Java library built on Apache Kafka, a distributed messaging system for real-time data streams.	Spark Streaming is a part of the Apache Spark ecosystem, a general-purpose big data processing engine.
Initial release	2016	2013
Processing model	Kafka Streams is a stream processing library that processes data records/events one at a time as they arrive in a stream. The processing logic assumes an independent record and some contextual/state information about the record. That limits the type of algorithms/computations you can implement in real-time.	Spark Streaming uses a micro-batch processing model, which simultaneously processes small batches of data records collected over time. The processing logic assumes you have all the related records available in the batch, allowing you to implement a wide range of algorithms/computations.
Fault tolerance	Kafka Streams leverages the built-in fault tolerance features of Kafka	Spark Streaming uses RDD (Resilient Distributed Datasets) to achieve fault tolerance.
Ease of use	Kafka Streams is known for its ease of use, as it has a simple and lightweight API designed to be developer-friendly.	Spark Streaming can be more complex to set up and configure, but it offers more features and tools for data processing and analysis.
Data sources and destinations	Can handle data from Kafka topics	Can handle data from Kafka topics and other sources like HDFS, AWS S3, data lakes, etc.
Integration	Kafka Streams is designed to work specifically with Kafka and requires a Kafka cluster to be set up.	It can run on various platforms, including Hadoop, Kubernetes, and Apache Mesos.
Managed cloud providers	Confluent, AWS MSK, Azure Event Hub, GCP Pub/Sub, etc.	DataBricks, AWS EMR, Azure HDInsight, GCP Dataproc, etc.
No-Code Low-Code API	kSQL	Spark SQL
When to go for	If your streaming application requires low latency processing of data from Kafka topics and you don’t need to process data from other sources,	If you need to process data from multiple sources or require a larger ecosystem and latency is not critical for your application.
Real-world examples	Airbnb: Airbnb uses Kafka Streams to process and analyze real-time data from their website, mobile applications, and other platforms to provide personalized recommendations to their users, optimize their operations, and detect fraudulent activities. Goldman Sachs: Goldman Sachs uses Kafka Streams to process and analyze real-time financial data from different sources to monitor their trading activities, detect anomalies, and optimize their trading strategies.	Uber: Uber uses Spark Streaming to process real-time data from their ride-hailing platform to monitor and improve the quality of their service, detect fraudulent activities, and optimize their operations. Netflix: Netflix uses Spark Streaming to analyze real-time customer data, monitor their streaming service, and perform real-time personalization to recommend personalized content to users.

Summary

Kafka Streams and Spark Streaming are potent tools for real-time data processing, but they have different strengths and weaknesses depending on the specific use case and requirements. All the above differences are based on my experiences and research and may not be accurate.

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