Introduction
A majority of successful businesses today are related to the field of technology and operate online. Their consumers’ activities create a large volume of data every second that needs to be processed at high speeds, as well as generate results at equal speed. These developments have created the need for data processing like stream and batch processing.
With this, big data can be stored, acquired, analyzed, and processed in numerous ways. Thus, continuous data streams or clusters can be queried, and conditions can be detected quickly, as soon as data is received. Apache Flink and Apache Spark are both open-source platforms created for this purpose.
However, as users are interested in studying Flink Vs. Spark, this article provides the differences in their features.
What is Apache Flink?
Apache Flink is an open-source framework for stream processing and it processes data quickly with high performance, stability, and accuracy on distributed systems. It provides low data latency and high fault tolerance. The significant feature of Flink is the ability to process data in real-time. It was developed by the Apache Software Foundation.
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What is Apache Spark?
Apache Spark is an open-source cluster computing framework that works very fast and is used for large scale data processing. It is built around speed, ease of use, and sophisticated analytics, which has made it popular among enterprises in varied sectors.
It was originally developed by the University of California, Berkeley, and later donated to the Apache Software Foundation.
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Flink Vs. Spark
Both Apache Flink and Apache Spark are general-purpose data processing platforms that have many applications individually. They can both be used in standalone mode, and have a strong performance.
They have some similarities, such as similar APIs and components, but they have several differences in terms of data processing. Given below is the list of differences when examining Flink Vs. Spark.
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Also Read: Spark Project Ideas & Topics
Conclusion
Both Flink and Spark are big data technology tools that have gained popularity in the tech industry, as they provide quick solutions to big data problems. But when analyzing Flink Vs. Spark in terms of speed, Flink is better than Spark because of its underlying architecture.
On the other hand, Spark has strong community support, and a good number of contributors. When comparing the streaming capability of both, Flink is much better as it deals with streams of data, whereas Spark handles it in terms of micro-batches.
Through this article, the basics of data processing were covered, and a description of Apache Flink and Apache Spark was also provided. The features of both Flink and Spark were compared and explained briefly, giving the user a clear winner based on the speed of processing. However, the choice eventually depends on the user and the features they require.
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What are the benefits of working with Apache Spark?
Apache Spark is also referred to as the “King” of Big Data, leaving behind plenty of reasons for users to consider it. When we work with Big Data, fast speed is mandatory and luckily, Spark’s speed matches the requirements. Apache Spark’s efficiency and speed make it desirable for use by data analytic engineers and data scientists. Compared to Hadoop, it is 100x fast. The next benefit of Apache Spark is how easy it is to use, especially when working with large datasets. Building parallel apps with more than 80 operators adds more ease. Apache Spark is dynamic as it is flexible and can develop parallel apps at once. Moreover, you can use any programming language such as Python, Java, Scala, etc. with Apache Spark due to its multilingual support.
What is the demand of Spark developers?
In addition to benefiting organizations, businesses, and industries, Apache Spark also benefits individuals. Spark developers, in present years, are very much in-demand. Companies are offering exquisite pay and benefits along with flexible work timings to hire the best surviving talent well-equipped with Apache Spark skills. The average salary of an Apache Spark developer in India is INR 7.2 LPA. Therefore, if you want growth, you can upskill yourself with Apache Spark. The Internet has plenty of data-related jobs that you can consider to enhance your understanding. However, it is best to take up a certification course or a hands-on tutorial for practical learning.
When should you choose Apache Flink?
When you need speed for fast processing, Apache Flink is your savior. It is faster than Apache Spark, which lets it process data briskly. Moreover, to work with complex stream processing, you must choose Apache Flink since its batch processing method is highly advanced. Batch processing is not possible in Apache Spark, thus, giving Apache Flink an upper hand. Furthermore, if you want to ignite your mind to learn the latest technology, Apache Flink is your new friend.
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