Kafka vs Spark – What’s the Difference ? (Pros and Cons)

Kafka vs Spark – What’s the Difference ? (Pros and Cons). The topic of today’s article will be the Kafka message broker, which needs no introduction. But, it is an open source platform dedicated to streaming distributed events. Also used by companies for high performance data flows, data integration and critical applications. 

Then, the second interesting software that we will discuss is Spark. All in all, it processes data that it executes very quickly and then processes it on very large data sets. Moreover, it also distributes data processing tasks to multiple computers.

Basically, this article focuses on both Kafka and Spark. As noted, we learn what they are and how they work. In addition, we will learn about their benefits, features, pluses and minuses. Finally, I will make a comparative analysis.

So let’s start with Kafka vs Spark – What’s the Difference ? (Pros and Cons).

What is Kafka?

Kafka is used for real time data streaming, big data collection, or real time analytics or both. Besides, Kafka is used with inn memory microservices to provide stability. Certainly it is  used to send events to complex event delivery system (CEP) and IOT/IFTTT style automation systems.

Additionally, the program runs on one or more servers. As a result, each node in the Kafka cluster is called a port. As shown, Kafka uses Apache ZooKeeper to manage groups. Particularly Broker’s job helps producer applications write data to subjects and consumer applications to read data from subjects. 

Concurrently, topics are divided into parts for easy management, and Kafka ensures that each part is reliably organized.

It is worth mentioning that codebase was originally developed at LinkedIn to provide a parallel loading mechanism. For Hadoop systems and became an open source project under the Apache Software Foundation in 2011.

How does Kafka work?

Firstly, editors are used to sending letters to Kafka topics. Then Kafka topic is divided into several sections. Sections in this topic are identified by the section key. Generally Kafka Broker generates this partition key using a different consensus algorithm than the message.

Now, let’s split the data on the Kafka topic into several small commands. There is a group of consumers who subscribe to a specific topic and receive data for processing. Whereas group of consumers who share a particular topic is called a consumer group.

Thus, each consumer holds the offset until it finishes processing the data. Thus, consumers are accustomed to consult the Kafka broker with the current delay and request the published data accordingly. 

If there is such data in the topic, the consumer will get that data. After receiving the data, the consumer will update its offset and mark the new offset as the mark to process the data point.

Features of Kafka

Usually persistent over the short term – You can store the data in Kafka for a short time. Then you can clear the memory and keep only the last value. Kafka’s default retention policy is set to 7 days, but you can configure it to your liking.

Replication – Kafka MirrorMaker provides transcription support for your cluster. With replication, messages can be replicated across multiple data centers or cloud regions. You can use it in active and passive backup and recovery scenarios. Also in passive and active scenarios to bring data closer to your users or to support data localization requirements.

Guarantee – Kafka will ensure that it will not produce duplicate messages in a topic. And that messages sent by producers to a particular topic section will be added in the order in which they were sent.

Fully configurable – Users can configure each Kafka feature.

Data transformations – Provides provisions for creating new data flows using product data flows.

Pros and cons of Kafka


Batch approach – Use a batch like use case. Moreover it can also be used as an ETL tool due to its data persistence capabilities.

Real time handling  Apache Kafka is capable of handling real time data pipelines. Building a real time data pipeline includes processing, analysis, storage, and more.

By default persistent – As we saw earlier, messages are persistent, which makes them permanent and reliable.

High concurrency – Capable of processing thousands of messages per second, also low latency and high performance. In addition, it allows reading and writing messages with high concurrency.

  • Ability to transfer large amounts of data consistently (non binary).
  • An abundance of options for managing and maintaining queues.
  • Easy expansion of topic partitions.
  • Every setting is configurable.


Clumsy behavior’s  – It usually behaves in an impractical way as the number of queues in the Kafka pool increases.

Lack some message paradigms – Some messaging models such as peer to peer queues, request/response, etc. In some cases Kafka is missing.

  • Interfaces can extend configuration properties.
  • A management interface would be nice.
  • Kafka tool is a community built Java application with a turn of the century look and feel.

Now with this article Kafka vs Spark – What’s the Difference ? (Pros and Cons) it is time to learn about Spark. 

What is Spark?

First of all,  Spark is an open source processing system used to process large data loads. Hence advantage is the optimization of in memory cache and fast analytical queries for data of any size. Moreover, it uses Java, Python and Scala API. Additionally, it supports code reuse for multiple workloads, including batch processing, real time analytics, machine learning, and graphics processing.

Generally, Spark is an open source processing system used to handle high data loads. However another advantage is memory caching and optimization of quick analytical queries regarding data of any size.

Following to another point it is definitely faster than previous approaches to working with Big Data, such as the classic MapReduce. That is a great secret of Spark’s speed is its operation on RAM. Which allows it to process faster than on disks.

How does Spark work?

By all means, the console runs its own Java process. These consoles communicate with a potentially large number of distributed workers called publishers. Each port is a separate Java process.

Further, the first layer of our famous software is the interpreter. Our open source software uses the Scala interpreter which has some modifications. When you enter your own code in the Spark console, the RDD is created and the operator data is applied, then the operator graph is also created. When the user starts an action, for example “collecting”, the chart is sent to the DAG schedule. 

Secondly, the DAG schedule divides the operator’s chart into stages (mapping and reduction). A stage consists of tasks based on splitting the input data. The DAG schedule combines pipe operators to optimize the chart. For, among others Multiple map operators can be scheduled in one step. This optimization is key to Sparks’ performance. 

Thirdly, the final result of the DAG is a set of stages. Later, the stages are transferred to the Task Scheduler. Likewise, the job scheduler runs jobs through the cluster manager(Spark Standalone / Mesos). For instance, the task scheduler does not know about the dependencies between stages.

Features of Spark

Real time stream processing – Stream provides the language’s Apache Spark API for manipulating streams. So you can write stream jobs just like batch jobs.

Supporting multiple languages – Built in multi language support. For example, it contains most of the APIs available in Java, Scala, Python, and R. Meanwhile the R language also offers advanced data analysis features. Likewise, Spark also includes SparkSQL, which has SQL like functionality. As a result, SQL developers find it very easy to use and the learning curve is reduced to an incredible level.

Better analytics – Unlike MapReduce, which includes both Map and Reduce functions, Spark offers more functionality. Markedly, Apache Spark includes a rich set of SQL queries, machine learning algorithms, complex analytics, and more. With all these features of Spark, big data analysis can be done in a better way.

Compatibility with hadoop – Not only can Spark run standalone, it can also run on top of Hadoop. Not only that, it is definitely compatible with both versions of the Hadoop ecosystem.

Pros and cons of Spark


  • Templates can be used if desired or desired, and blank/custom options if desired/required.
  • The video walks users through the process of setting up and provides suggestions.
  • With an account, all projects are stored in the cloud and are accessed from anywhere.
  • Creative commons licensed images, icons, and graphics.
  • Link to or download Spark creations.
  • Different layout styles and options.
  • Data frame as a distributed collection of data: easy for developers to implement algorithms and formulas.


  • It would be great if Apache Spark could provide a built in database to handle all the file information for registered decks.
  • If using the voice over tool, it should be used in a quiet space.
  • The graphics produced by Apache Spark are by no means world class. Sometimes they look like high school students.
  • Spark may behave differently in some browsers. For example, slides can be moved in Firefox, but sometimes cannot be moved in Chrome.

Comparison of Kafka vs Spark

Although people’s interest in both Kafka and Spark is quite similar; there are important differences between the two, wait and see.


Conversely, latency is a common cause and if you need real time processing, where the timeframe is less than milliseconds, choose Kafka. When looking at event driven processing, Kafka is number one because it has excellent fault tolerance. And its compatibility with other types of systems can seem quite complicated.

If you are comfortable with latency and want very good source flexibility and compatibility. You should choose Spark as it will be a more suitable option.

Programming languages support

We know that none of the programming languages are supported in any way by Kafka for data transformation.

However, with Spark it supports different programming languages and structures. Consequently, we know that Spark has the ability to do more than just interpret data. It is able to use existing machine structures and process diagrams.

Processing type

One side, Kafka analyses events as they progress. Overall, it uses the model of continuous processing, i.e. events over time.

The second case is Spark, which uses a micro batch processing approach so that it breaks the incoming streams into smaller batches for processing.


Here, Kafka does not provide exclusive ETL services. Instead, it uses the Kafka Connect API and the Kafka Streams API to stream data from source to destination. Through the Kafka Connect API, Kafka allows you to create data streams (E and L in ETL). 

The Connect API takes advantage of Kafka’s scalability, as it is based on Kafka’s failover plan. For that reason, it provides a single way to monitor all connections. The Kafka Streams API, which provides T in ETL, can be used for stream processing and transformations.

Because Spark allows users to retrieve data, store it, manipulate it. And move it from source to destination, it enables the ETL process.


Particularly here, Kafka provides replication of data in the cluster for recovery. Which requires repeated copying and distribution of data to other servers or brokers. When one of the Kafka servers is running, your data is available on other servers that you can easily access.

In contrast, Apache Spark tolerates worker node failures in a cluster with Sparks RDD, preventing data loss. All changes and actions are permanently saved, so you try all these steps again and if you fail you get the same results.

Kafka vs Spark

Comparison table Kafka Spark
Brief description
Speaking of a platform, which is famous for distributed streaming. It allows developers to create interesting applications, thanks to which they can continuously generate and consume data streams.
The next step in the comparison will be a general purpose distributed computing system. It is mainly designed to handle high data loads, and in addition it uses memory caching. Moreover, it will optimize the query execution function, just to ensure quick analytical queries on data of any size.
Ecosystem components
Kafka Topics, Producers, Brokers, Consumers, Kafka streams API need to be used.
Spark Core, SQL, Spark streaming and structured streaming, MLlib, GraphX.
Fault tolerance
Information is copied to other brokers. If a broker goes down, Zookeeper will look for another broker to accept the load.
Data is separated into durable distributed data sets (RDDs). If a node fails, its content is recalculated from the original data.
This is a Java client library. This allows it to run anywhere Java is supported.
It runs on top of the Spark stack. It can be either standalone Spark, YARN or container based.

Thank you for reading Kafka vs Spark – What’s the Difference ? (Pros and Cons). Let’s conclude. 

Kafka vs Spark – What’s the Difference ? Conclusion

This article introduced two of the most popular Apache big data processing tools, Apache Kafka and Apache Spark. It provides an overview of their benefits, workflows and fundamental differences. So you can make better informed decisions and process the data according to your different needs. Think which one is more suitable Apache Kafka vs Spark game.

We can use Kafka as a message broker. It can store data for a certain period of time. Importantly, Kafka allows us to perform window operations in real time. But we can’t change ETL in Kafka. With Spark, we can store data in a data object and perform deep ETL transformations.

Visit our website to find out more about Kafka and Spark.

Avatar for Kamil Wisniowski
Kamil Wisniowski

I love technology. I have been working with Cloud and Security technology for 5 years. I love writing about new IT tools.

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