What’s the Difference Between Kafka and Spark?

Apache Kafka is a stream processing engine and Apache Spark is a distributed data processing engine. In analytics, organizations process data in two main ways—batch processing and stream processing. In batch processing, you process a very large volume of data in a single workload. In stream processing, you process small units continuously in real-time flow. Originally, Spark was designed for batch processing and Kafka was designed for stream processing. Later on, Spark added the Spark Streaming module as an add-on to its underlying distributed architecture. However, Kafka offers lower latency and higher throughput for most streaming data use cases.

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What are the similarities between Kafka and Spark?

Both Apache Kafka and Apache Spark are designed by the Apache Software Foundation for processing data at a faster rate. Organizations require modern data architecture that can ingest, store, and analyze real-time information from various data sources.

Kafka and Spark have overlapping characteristics to manage high-speed data processing. 

Big data processing

Kafka provides distributed data pipelines across multiple servers to ingest and process large volumes of data in real time. It supports big data use cases, which require efficient continuous data delivery between different sources.

Likewise, you can use Spark to process data at scale with various real-time processing and analytical tools. For example, with Spark's machine learning library, MLlib, developers can use the stored big datasets for building business intelligence applications.

Read about business intelligence »

Data diversity

Both Kafka and Spark ingest unstructured, semi-structured, and structured data. You can create data pipelines from enterprise applications, databases, or other streaming sources with Kafka or Spark. Both data processing engines support plain text, JSON, XML, SQL, and other data formats commonly used in analytics.

They also transform data before they move it into integrated storage like a data warehouse, but this may require additional services or APIs. 


Kafka is a highly scalable data streaming engine, and it can scale both vertically and horizontally. You can add more computing resources to the server hosting a specific Kafka broker to cater to growing traffic. Alternatively, you can create multiple Kafka brokers on different servers for better load balancing.

Likewise, you can also scale Spark's processing capacity by adding more nodes to a cluster. For instance, it uses Resilient Distributed Datasets (RDD) that store logical partitions of immutable data on multiple nodes for parallel processing. So, Spark also maintains optimum performance when you use it to process large data volumes. 

Workflow: Kafka vs. Spark

Apache Kafka and Apache Spark are built with different architectures. Kafka supports real-time data streams with a distributed arrangement of topics, brokers, clusters, and the software ZooKeeper. Meanwhile, Spark divides the data processing workload to multiple worker nodes, and this is coordinated by a primary node. 

How does Kafka work?

Kafka connects data producers and consumers using a real-time distributed processing engine. The core Kafka components are these:

  • A broker that facilitates transactions between consumers and producers
  • A cluster that consists of multiple brokers residing in different servers

Producers publish information to a Kafka cluster while consumers retrieve them for processing. Each Kafka broker organizes the messages according to topics, which the broker then divides into several partitions. Several consumers with a common interest in a specific topic may subscribe to the associated partition to start streaming data. 

Kafka retains copies of data even after consumers have read it. This allows Kafka to provide producers and consumers with resilient and fault-tolerant data flow and messaging capabilities. Moreover, ZooKeeper continuously monitors the health of all Kafka brokers. It ensures that there is a lead broker that manages other brokers at all times. 

How does Spark work?

The Spark Core is the main component that contains basic Spark functionality. This functionality includes distributed data processing, memory management, task scheduling and dispatching, and interaction with storage systems. 

Spark uses a distributed primary-secondary architecture with several sequential layers that support data transformation and batch processing workflows. The primary node is the central coordinator that schedules and assigns data processing tasks to worker nodes. 

When a data scientist submits a data processing request, the following steps occur:

  1. The primary node creates several immutable copies of the data
  2. It uses a graph scheduler to divide the request into a series of processing tasks
  3. It passes the tasks to the Spark Core, which schedules and assigns them to specific worker nodes

Once the worker nodes complete the tasks, they return the results to the primary node through the cluster manager. 

Key differences: supervised vs. unsupervised learning

In supervised learning, an algorithm can be trained with labeled images of bananas to recognize and count them accurately. On the other hand, unsupervised learning would group the images based on similarities. The model could potentially identify different varieties of bananas or group them with other fruits without explicitly knowing they’re bananas. We discuss some more differences next.


The main goal of supervised learning is to predict an output based on known inputs.

However, the main goal of unsupervised learning is to identify valuable relationship information between input data points, apply the information to new inputs, and draw similar insights.


Supervised learning aims to minimize the error between predicted outputs and true labels. It generalizes the learned relationships to make accurate predictions on unseen data.

In contrast, unsupervised machine learning models focus on understanding the inherent structure of data without guidance. They prioritize finding patterns, similarities, or anomalies within the data.


Both supervised and unsupervised learning techniques vary from relatively basic statistical modeling functions to highly complex algorithms, depending on the problem set.

Supervised learning applications are widespread and non-technical users can also develop custom solutions based on preexisting models.

In contrast, unsupervised learning applications can be more difficult to develop, as the possibility of patterns and relationships in data is vast.

Key differences: Kafka vs Spark

Both Apache Kafka and Apache Spark provide organizations with fast data processing capabilities. However, they differ in architectural setup, which affects how they operate in big data processing use cases.


Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository. It requires data transformation capabilities to transform diverse data into a standard format.

Spark comes with many built-in transform and load capabilities. Users can retrieve data from clusters and transform and store them in the appropriate database. 

On the other hand, Kafka does not support ETL by default. Instead, users must use APIs to perform ETL functions on the data stream. For example:

  • With Kafka Connect API, developers can enable extract (E) and load (L) operations between two systems
  • Kafka Streams API provides data transformation (T) features that developers can use to manipulate the event messages into a different format

Read about ETL »


Spark was developed to replace Apache Hadoop, which couldn't support real-time processing and data analytics. Spark provides near real-time read/write operations because it stores data on RAM instead of hard disks. 

However, Kafka edges Spark with its ultra-low-latency event streaming capability. Developers can use Kafka to build event-driven applications that respond to real-time data changes. For example, The Orchard, a digital music provider, uses Kafka to share siloed application data with employees and customers in near real time.

Read how The Orchard works with AWS »

Programming languages

Developers can use Spark to build and deploy applications in multiple languages on the data processing platform. This includes Java, Python, Scala, and R. Spark also offers user-friendly APIs and data processing frameworks that developers can use to implement graph processing and machine learning models.

Conversely, Kafka doesn't provide language support for data transformation use cases. So, developers can’t build machine learning systems on the platform without additional libraries. 


Both Kafka and Spark are data processing platforms with high availability and fault tolerance.

Spark maintains persistent copies of workloads on multiple nodes. If one of the nodes fails, the system can recalculate the results from the remaining active nodes. 

Meanwhile, Kafka continuously replicates data partitions to different servers. It automatically directs consumer requests to the backups if a Kafka partition goes offline. 

Multiple data sources

Kafka streams messages from multiple data sources concurrently. For example, you can send data from different web servers, applications, microservices, and other enterprise systems to specific Kafka topics in real time.

On the other hand, Spark connects to a single data source at any one time. However, using the Spark Structured Streaming library allows Spark to process micro-batches of data streams from multiple sources.

Key differences: Kafka vs. Spark Structured Streaming

Spark Streaming allows Apache Spark to adopt a micro-batch processing approach for incoming streams. It has since been enhanced by Spark Structured Streaming, which uses DataFrame and Dataset APIs to improve its stream processing performance. This approach allows Spark to process continuous data flow like Apache Kafka, but several differences separate both platforms. 

Processing model

Kafka is a distributed streaming platform that connects different applications or microservices to enable continuous processing. Its goal is to ensure client applications receive information from sources consistently in real time.

Unlike Kafka, Spark Structured Streaming is an extension that provides additional event streaming support to the Spark architecture. You can use it to capture real-time data flow, turn data into small batches, and process the batches with Spark's data analysis libraries and parallel processing engine. Despite that, Spark streaming cannot match Kafka's speed for real-time data ingestion. 

Data storage

Kafka stores messages that producers send into log files called topics. The log files need persistent storage to ensure the stored data remains unaffected in case of a power outage. Usually, the log files are replicated on different physical servers as backups.

Meanwhile, Spark Structured Streaming stores and processes data streams in RAM, but it might use disks as secondary storage if data exceeds the RAM's capacity. Spark Structured Streaming seamlessly integrates with Apache Hadoop Distributed File System (HDFS), but it also works with other cloud storage, including Amazon Simple Storage Service (Amazon S3).


Kafka allows developers to publish, subscribe, and set up Kafka data streams, then process them with different APIs. These APIs support a wide range of programming languages, including Java, Python, Go, Swift, and .NET.

Meanwhile, Spark Structured Streaming's APIs focus on data transformation on live input data ingested from various sources. Unlike Kafka, Spark Structured Streaming APIs are available in limited languages. Developers can build applications using Spark Structured Streaming with Java, Python, and Scala.

When to use: Kafka vs. Spark

Kafka and Spark are two data processing platforms that serve different purposes.

Kafka allows multiple client apps to publish and subscribe to real-time information with a scalable, distributed message broker architecture. On the other hand, Spark allows applications to process large amounts of data in batches.

So, Kafka is the better option for ensuring reliable, low-latency, high-throughput messaging between different applications or services n the cloud. Meanwhile, Spark allows organizations to run heavy data analysis and machine learning workloads. 

Despite their different use cases, Kafka and Spark are not mutually exclusive. You can combine both data processing architectures to form a fault-tolerant, real-time batch processing system. In this setup, Kafka ingests continuous data from multiple sources before passing them to Spark's central coordinator. Then, Spark assigns data that requires batch processing to respective worker nodes.

Summary of differences: Kafka vs. Spark





Needs Kafka Connect API and Kafka Streams API for ETL functions.

Supports ETL natively.


Ultra-low latency. Provides true real time for each incoming event. 

Low latency. Performs read/write operations on RAM.

Programming languages

Needs additional libraries to implement data transformation functions. 

Supports Java, Python, Scala, and R for data transformation and machine learning tasks. 


Backup data partition at different servers. Direct requests to backups when an active partition fails. 

Maintains persistent data at multiple nodes. Recalculates the result when a node fails. 

Data sources

Can support multiple data sources concurrently.

Connects to a single data source. Needs Spark Structured Streaming to stream with multiple data sources.

How can AWS help with your Kafka and Spark requirements?

Amazon Web Services (AWS) provides managed data infrastructure support whether you're using Apache Kafka or Apache Spark.

Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to deploy, run, and manage your Kafka clusters effortlessly. It can automatically do the following:

  • Provision all the necessary resources for the entire Kafka clusters
  • Replicate and distribute Kafka clusters across multiple Availability Zones
  • Run your Kafka clusters in Amazon Virtual Private Cloud (Amazon VPC) to provide private connectivity between nodes

Use Amazon EMR to support your Spark big data, interactive analytics, and machine learning applications. With Amazon EMR, you can do the following:

  • Save over half the cost of an on-premises data processing solution.
  • Automatically provision compute resources for your big data applications to meet changing demands.
  • Integrate Spark with various scalable cloud storages, including Amazon S3, Amazon DynamoDB, and Amazon Redshift

Get started with Spark and Kafka on AWS by creating an account today.

Next Steps with AWS

Start building with Kafka

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