Find All The Key Differences Between Apache Spark Vs. Apache Kafka

The word Big Data has gained significant popularity over the years. Regardless of your industry or company size, the complexity and growing volume of Big Data make it necessary for data collection, comprehension, and analytics.

You can find various Big Data technologies on the market, including Apache Kafka, Apache Spark, and others. In this thorough blog, we will discuss the two primary prominent tools: Apache Kafka and Apache Spark.

Apache Kafka

Kafka is a portioned, distributed, and replicated log service accessible as a free streaming platform. Apache Kafka is written in Scala and Java, which helps to pull the data from a wide range of sources and store it in the form of topics by processing the data stream. These topic messages are stored for long periods with applications that process them to provide efficient insights.

Following are the benefits of Apache Kafka:

• All the logs are maintained with the punched time without needing to delete the data. It reduces the data loss risk.
• It offers load de-bulking as no indexes are needed for the messages.
• It can enhance streaming efficiency and reduce buffering for end users.

Apache Kafka Workflow

Kafka is a famous publish-subscribe messaging system that will help you handle the amount of data and manage online and offline communications. Message producers are also called publishers. It also messages the consumers who are known as the subscribers in the publish-subscribe system.

You can exchange messages in this domain with the help of the destination known as the topic. A publisher will create the messaging for the topic. In comparison, the subscribers who have subscribed to the topic consume the messages.

The technology will allow the messages to be broadcast to more than one subscriber, and all of them will get a copy of the messages published on the particular topic. The Kafka messages will be stored on the disk and replicated throughout the broker cluster as it helps to prevent data loss.

Additionally, Apache Kafka will employ the distributed messaging paradigm. It helps to entail the non-synchronous message queuing between the messaging systems and apps. This platform will allow you to transport the messages from one end to the other, and it is best for offline and online message consumption.

Kafka offers a queue-based messaging system that is efficient, quick, resilient, and fault-tolerant with minimal downtime. Several consumers with the same group ID can subscribe to the subject in the queue messaging system. They are considered to be a single unit and share the same messages.

Apache Spark

Spark is a cluster computing framework that is open-source and free to use. The data processing solution helps deal with large workloads and data collection. It can quickly handle the massive data volumes and divide the jobs across the system to reduce the significant workload. With the help of the DAG scheduler, engine, and query optimizer, Spark is known for its high-performance quality for batch and streaming data processing.

Following are the benefits of Apache Spark:

• Spark can assist with advanced analytics such as graph processing and machine learning. It is equipped with amazing libraries such as DataFrames, SQL and MLlib, Spark Streaming, and GraphX that help companies solve complex data issues without hassle. Additionally, Spark also enhances the analytics performance by storing the data in the RAM of the servers. It is highly accessible.
• Spark is known for leveraging Hadoop’s cluster management and underlying storage so that it can run as a single engine. It can work independently of Hadoop to collaborate with the cluster administrators and storage solutions such as the Amazon S3 and Cassandra.

Apache Spark Workflow

The architecture of the platform is based on the RDDs (Resilient Distributed Dataset) and DAG (Directed Acyclic Graph). It is the collection of the data objects partitioned, which will help store the memory on the Spark cluster’s worker nodes. Spark supports the two different types of RDDs when it comes to datasets. The Hadoop datasets are built from the HDFS files and parallelized collections. It is based on the current Scala collection.

The DAG is a group of data-processing operations in which all of the nodes represent the RDD division, and all of the edges are responsible for representing the data transformation. The DAG abstraction will eliminate Hadoop’s multi-stage MapReduce execution model and enhance its performance over Hadoop.

Apache Spark uses the slave architecture comprising the central coordinator and the distributed workers. When making a spark request, the driver program will run and look for sources from the cluster manager while launching the primary program of the user processing program’s user function.

The logic for execution will be processed, and the platform’s context will be built in parallel. The several transformations and actions are done with the help of the Spark context. Until the action is encountered, all transformations are kept in the Spark context as the DAG, which will be the RDD lineage.

Understanding The Differences Between Apache Spark vs Apache Kafka

Here is a quick comparison between Apache Spark Vs Apache Kafka:

Apache Spark Vs Kafka: ETL (Extract, Transform and Load)

As Spark helps users to pull the data, process, and push from the source for targeting, it allows for the best ETL processes while as Kafka does not offer exclusive ETL services. Rather, it depends on the Kafka Connect API, and the Kafka streams API to build the streaming data pipelines from the destination source.

The Kafka API will allow for the creation of the streaming data pipelines. The connect API uses the scalability of Kafka, which is built on Kafka’s fault tolerance design and offers a unified method of monitoring all the connections. The Kafka Streams API offers the T in ETL, which you can use to add stream processing and transformations.

Recovery

A real-time processing system should be accessible 24*7 as it necessitates the ability to recover from various system faults. Apache Spark can easily handle the worker node failures in the clusters due to the efficiency of the Spark RDDs, which protect the data. All the actions and transformations are secured, allowing you to retry all the stages of any potential failure and help you get identical outcomes.

Whereas Kafka offers data replication within the cluster for recovery, it entails duplicating and data distribution often to other servers or brokers. If one of the Kafka servers is not working, you can access the data through other servers.

Latency

If latency is not an issue for you and you are looking for source flexibility with compatibility, then choosing Spark will be a better option. However, if real-time processing and latency are your primary concern, the best choice is to go for Kafka. Due to the event-driven processing, Kafka offers better fault tolerance. However, the compatibility with other systems can get complicated.

Programming Languages Compatibility

While Kafka does not support any programming language for data transformation, Spark is known for supporting various programming languages and frameworks. In other words, Apache Spark can potentially do more than just interpret the data as it can employ the existing machine learning frameworks and process graphs.

Processing Type

Kafka analyses the event as they usually unfold. It results in a continuous processing model. Spark uses the micro-batch processing approach to divide the incoming streams into small batches for processing.

Choose The Right Implementation Partner

With the help of the appropriate Big Data tools, you can easily transform the raw data into a form that will help your company to make better decisions for a streamlined process. It makes it highly critical to make better decisions to have an effective data processing tool. Ksolves is here to help.

Ksolves is a leading Big Data analytics services provider with more than ten years of experience. You can connect to us at sales@ksolves.com. We will help you pick the right Big Data tool for your business. Moreover, we are open to multiple iterations in the implementation process for customisations and improvements.

Conclusion

The Blog includes the two most popular data processing tools of Apache foundation. With this article, you will get an overview of their benefits, primary differences, and workflows that can help you to make better decisions and process information of varying needs before choosing Spark or Kafka.