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