Spark vs. Kafka

We are gradually adjusting to the new digital world where many manual tasks have been automated using sophisticated devices. Companies today have become more data-dependent to make efficient business decisions and offer better customer experience through their products and services. A massive amount of data is generated every day which is in the raw format. Big data analytics is required to find hidden trends from this raw data and draw meaningful insights that can help in decision making.

Big data has evolved over the years and many tools have been developed to drive the real value of the technology. For example, in the initial use of big data, Hadoop and its related services like MapReduce and Pig were utilized to explore its basic capabilities. Many advancements then took place like separating storage and processing, demand for stream processing, and the need to use cloud services for the deployment of big data. This led to the introduction of tools and frameworks like Apache Spark, Apache Kafka, Amazon EMR, and Amazon S3 in the big data market.

This article particularly focuses on Apache Spark and Apache Kafka, both of which are popularly used for real-time processing of data concurrently and continuously. You’ll learn about Spark vs. Kafka and why taking up an online course, like the Spark training program, is beneficial if you are seeking a career in Big Data.

What is Apache Spark?

As mentioned on its official website, Apache Spark is a unified analytics engine for large-scale data processing. Developed to support iterative jobs on distributed datasets, Spark is complementary to Hadoop and can run on top of it. Building large-scale and low-latency data analytics applications is effortlessly achieved through Spark. It is an open-source framework majorly known for its performance being 100 times faster than Hadoop for large scale data processing.

Due to its massive computing power, Spark is quite suitable for machine learning and graph processing. Using Spark, developers can easily write applications in languages like Java, Scala, R, Python, and SQL. It also supports more than 80 high-level operators that are useful in building parallel apps. Its useful group of libraries includes SQL and DataFrames, GraphX, Spark Streaming, and MLib (for machine learning).

There are thousands of companies using Spark in production, along with renowned names like Cisco, Amazon, Baidu, NTT Data, Shopify, and Yahoo. Some of the clients are running Spark on as high as 8000 clusters containing thousands of nodes. It is proven to sort around 100TB of data three times faster than Hadoop MapReduce on 1/10th of the machines. Industrial sectors apart from the IT industry are also utilizing Spark like finance, retail, travel, healthcare, and media.

What is Apache Kafka? defines Kafka as an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Using Kafka, you can deliver messages at network limited throughput using a cluster of low latency machines, scale production clusters up to a thousand brokers, store streams of data safely in a fault-tolerant cluster, and stretch clusters efficiently over availability zones.

Apache Kafka can be used for a wide range of applications like processing payments and financial transactions in real-time, capturing and analyzing sensor data from IoT devices continuously, instantly reacting to customer interactions and orders, and monitoring patients in hospital care. All these tasks are accomplished by Kafka through three of its key capabilities – read and write streams of events which includes import or export of data from other systems, storing those streams reliably and durably, and processing those streams of events as they occur.

You would be fascinated to know that Kafka is used by 80% of all Fortune 100 companies. It is used in industrial sectors like manufacturing, banking institutes, insurance, IT, and telecom. Some of the top companies using Apache Kafka include LinkedIn, Netflix, Twitter, Uber, Goldman Sachs, PayPal, Airbnb, and Oracle.

Spark vs. Kafka

Both Apache Spark and Kafka have their own set of pros and cons. What you should select is based on certain considerations – if you want to run stream processing on a cluster manager, latency guarantees, data sinks, if the processing is data-parallel or task-parallel, what ecosystem you are using, the community adoption, and so on. Spark is a general choice for companies that want to run the application on shared infrastructure hence can integrate spark applications with existing Hadoop distributions.

On the other side if it is required to manage an application with a stand-alone program without the need for a cluster manager obvious choice would be Kafka. Another consideration is to assess if the data sink is Kafka or HDFS based. Spark system aligns well with the Hadoop ecosystem while Kafka data sinks fit effortlessly with Kafka streams. Spark has an edge if latencies in the order of seconds is acceptable and event time is irrelevant, Kafka on the other hand is suitable for low latency.

To conclude, it entirely depends on the type of use case or application to be developed. Both Spark or Kafka can be used by comparing the parameters described above with respect to the requirement to be fulfilled. Hope it has answered some of your questions or at least points you in the right direction.

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