Apache Kafka vs Apache Spark

Apache Kafka Vs Apache Spark: Know the Differences

Apache Kafka vs Apache Spark is the comparison of two popular technologies that are related to big data processing are known for fast and real-time or streaming data processing capabilities. Kafka is an open-source tool that generally works with the publish-subscribe model and is used as intermediate for the streaming data pipeline. Spark is a known framework in the big data domain that is well known for high volume and fast unstructured data analysis. The basic storage components in Kafka is known as the topic for producer and consumer events. whereas Spark used Resilient distributed dataset structure (RDD) and Data frames for processing the data sets.

Apache Kafka:

Kafka is an open-source stream processing platform developed by the Apache. It is a mediator between source and destination for a real-time streaming process where we can persist the data for a specific time period. Kafka is a distributed messaging system. Where we can use that persisted data for the real-time process. It runs as a service on one or more servers. The Kafka stores stream of records in categories called topics. Each stream record consists of key, value, and timestamp.

Following are the main component of Kafka


Source: This will trigger when a new CDC (Change Data Capture) or new insert occurs at the source. For that, we have to define a key column to identify the change.

Broker: Which is responsible for holding data. Each Broker holds no of partition.

Topic: It categorizes the data. Topics in Kafka are always subscribed by multiple consumers that subscribe to the data written to it.

Partition: Topics are further splited into partition for parallel processing.

Producer: Producer is responsible for publishing the data. It will push the data to the topics of their choice. The producer will choose which record to assign to which partition within the topic.

Consumer: Consumers will consume data from topics. A consumer will be a label with their consumer group. If the same topic has multiple consumers from different consumer group then each copy has been sent to each group of consumers.

Kafka has better throughput and has features like built-in partitioning, replication, and fault-tolerance which makes it the best solution for huge scale message or stream processing applications.

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.


Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley’s Amp Lab, the Spark codebase was later donated to the Apache Software Foundation. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.


Spark is the platform where we can hold the data in Data Frame and process it. Application developer, Data Scientist, Data Analyst can use the Spark to process the huge amount of data within a minimum period of time. We can use a feature like interactive, iterative, analysis of data in Spark.


Spark Streaming:

Spark Streaming receives live input data streams, it collects data for some time, builds RDD, divides the data into micro-batches, which are then processed by the Spark engine to generate the final stream of results in micro-batches. Following data flow diagram explains the working of Spark streaming.

Spark Streaming provides a high-level abstraction called discretized stream or DStream, which represents a continuous stream of data.

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.

Spark streaming is one more feature where we can process the data in real-time. The banking domain need to track the real-time transaction to offer the best deal to the customer, tracking suspicious transactions. Spark streaming is most popular in younger Hadoop generation. Spark is a lightweight API easy to develop which will help a developer to rapidly work on streaming projects. Spark streaming will easily recover lost data and will be able to deliver exactly once the architecture is in place. And without any extra coding efforts We can work on real-time spark streaming and historical batch data at the same time (Lambda Architecture).

In Spark streaming, we can use multiple tools like a flume, Kafka, RDBMS as source or sink Or we can directly stream from RDBMS to Spark.


Apache Kafka Vs Apache Spark:

Apache Kafka Apache Spark
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.
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.
It processes data from Kafka itself via topics and streams. Spark ingest data from various files, Kafka, Socket source, etc.
It supports Java mainly. It supports multiple languages such as Java, Scala, R, Python.
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.
Kafka is a Message broker. Spark is the open-source platform.
Kafka has Producer, Consumer, Topic to work with data. Spark provides platform pull the data, hold it, process and push from source to target.
Kafka provides real-time streaming, window process. Spark allows for both real-time stream and batch process.
In Kafka, we cannot perform a transformation. In Spark we perform ETL (Extract, Transform, and Load)
Kafka does not support any programming language to transform the data. Spark supports multiple programming languages and libraries.
Kafka is used for real-time streaming as Channel or mediator between source and target. Spark uses for a real-time stream, batch process and ETL also.
Kafka store data in Topic i.e in a buffer memory. Spark uses RDD to store data in a distributed manner (i.e cache, local space)
Decent speed 100 times faster than Hadoop
Easy to configure Easy to learn because of high-level modules
Fault-tolerant/Replication Allows recovery of partitions using Cache and RDD


We can use Kafka as a message broker. It can persist the data for a particular period of time. Using Kafka we can perform real-time window operations. But we can’t perform ETL transformation in Kafka. Using Spark we can persist data in the data object and perform end-to-end ETL transformations. I hope you have understood what is apache kafka, apache spark and Apache Kafka vs Apache Spark.

So it’s the best solution if we use Kafka as a real-time streaming platform for Spark. This is all about Apache Kafka Vs Apache Spark, let us know if more information or posts needed on this topic.

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