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Kafka Streams Vs. Spark Streaming

Apache Spark

Apache Spark is a distributed and a general processing system which can handle petabytes of data at a time. It is mainly used for streaming and processing the data. It is distributed among thousands of virtual servers. Large organizations use Spark to handle the huge amount of datasets. Apache Spark allows to build applications faster using approx 80 high-level operators. It gains high performance for streaming and batch data via a query optimizer, a physical execution engine, and a DAG scheduler. Thus, its speed is hundred times faster.

Spark Streaming

Apache spark enables the streaming of large datasets through Spark Streaming. Spark Streaming is part of the core Spark API which lets users process live data streams. It takes data from different data sources and process it using complex algorithms. At last, the processed data is pushed to live dashboards, databases, and filesystem.

Kafka Streams

A client library to process and analyze the data stored in Kafka. Kafka streams enable users to build applications and microservices. Further, store the output in the Kafka cluster. It does not have any external dependency on systems other than Kafka. It only processes a single record at a time.

Kafka Streams Vs. Spark Streaming

Kafka Streams vs Spark Streaming

Parameters Apache Kafka Apache Spark
Developers Originally developed by LinkedIn. Later, donated to Apache Software Foundation. Originally developed at the University of California. Later, it was donated to Apache Software Foundation.
Infrastructure It is a Java client library. Thus, it can execute wherever Java is supported. It executes on the top of the Spark stack. It can be either Spark standalone, YARN, or container-based.
Data Sources It processes data from Kafka itself via topics and streams. Spark ingest data from various files, Kafka, Socket source, etc.
Processing Model It processes the events as it arrives. Thus, it uses Event-at-a-time (continuous) processing model. It has a micro-batch processing model. It splits the incoming streams into small batches for further processing.
Latency It has low latency than Apache Spark It has a higher latency.
ETL Transformation It is not supported in Apache Kafka. This transformation is supported in Spark.
Fault-tolerance Fault-tolerance is complex in Kafka. Fault-tolerance is easy in Spark.
Language Support It supports Java mainly. It supports multiple languages such as Java, Scala, R, Python.
Use Cases The New York Times, Zalando, Trivago, etc. use Kafka Streams to store and distribute data., Yelp (ad platform) uses Spark streams for handling millions of ad requests per day.

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