{"id":166548,"date":"2023-02-07T10:09:19","date_gmt":"2023-02-07T09:09:19","guid":{"rendered":"https:\/\/liora.io\/en\/?p=166548"},"modified":"2026-02-27T12:11:36","modified_gmt":"2026-02-27T11:11:36","slug":"apache-spark-its-functions-and-benefits","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/apache-spark-its-functions-and-benefits","title":{"rendered":"Apache Spark: Understanding its Functions and Benefits"},"content":{"rendered":"Apache Spark is a unified, ultra-fast analytics engine for large-scale data processing. It enables large-scale analysis through cluster machines. It is mainly dedicated to Big Data and Machine Learning.<\/strong>

What is Apache Spark?<\/h3>\nFor the curious, let’s go back to the creation of Apache Spark!\n\nIt all started in 2009. Spark<\/a><\/strong> was designed by Matei Zaharia, a Canadian computer scientist, during his PhD at the University of California at Berkeley. Initially, its development is a solution to accelerate the processing of Hadoop systems<\/b>.\n\nToday it is a project of the Apache Foundation. Since 2009, more than 1200 developers have contributed to the project. Some of them are from well-known companies like Intel, Facebook, IBM, Netflix…\n\nIn 2014, Spark officially set a new record in large-scale sorting. It won the Daytona Grey Sort competition by sorting 100 TB of data in just 23 minutes<\/b>. The previous world record was 72 minutes set by Yahoo using a 2100-node MapReduce Hadoop cluster, while Spark uses only 206 nodes. This means it sorted the same data three times faster using ten times fewer machines. \n\nFurthermore, while there is no official petabyte sorting competition, Spark goes even further by sorting 1 PB of data, which is equivalent to 10 trillion records, on 190 machines in less than four hours. \n\nThis was one of the first petabyte-scale sorts<\/b> ever done in a public cloud. Achieving this benchmark marks a significant milestone for the Spark project. It proves that Spark is delivering on its promise to serve as a faster, more scalable engine<\/b> for processing data of all sizes, from GBs to TBs even going to PBs.\n

Apache Spark: the largest open source Big Data project<\/h3>\nOriginally developed at UC Berkeley in 2009, Apache Spark is a unified analytical engine for Big Data and Machine Learning<\/a><\/strong>. The tool is distinguished by its impressive speed and ease of use.\n\nSince its launch, Apache Spark has been adopted by many companies<\/b> in a wide variety of industries. Internet giants like Netflix, Yahoo and eBay have deployed Spark and are processing multiple petabytes of data on clusters of over 8,000 nodes.\n\nIn just a few years, Apache Spark has quickly become the largest open source Big Data project<\/b>. It has over 1000 contributors from more than 250 organizations.\n\nThis 100% open source project is hosted by the Apache Software Foundation. However, Apache Spark, Spark and the Spark logo are trademarks of the ASF.\n\nAs a non-profit organization, the ASF must take precautions about how its trademarks are used<\/b> by organizations. In particular, it must ensure that its software products are clearly distinguishable from all potential third-party products.\n\nCompanies wishing to provide Apache Spark-based software, services, events, and other products should refer to the foundation’s trademark policy and FAQ.\n\nCommercial or open source software products are not allowed to use Spark in their name<\/b>, except as “powered by Apache Spark” or “for Apache Spark”. Strict rules must be followed.\n\nNames derived from “Spark” such as “Sparkly” are also not allowed, and company names may not include “Spark”. Package identifiers may contain the word “spark”, but the full name used for the software package must follow the rules.\n\nWritten material must refer to the project as “Apache Spark”<\/b> in the first mention, and logos derived from Spark’s are not allowed. Finally, domain names containing “Spark” are not allowed without written permission from Apache Spark PMC.\n

What are the benefits of Spark?<\/h3>\nAs you might have guessed, the main advantage of Spark is its speed<\/b>. Spark was designed from the ground up with performance in mind. It uses in-memory computing and other optimizations for this. \n\nToday it is estimated to be 100 times faster than Hadoop<\/b> for data processing, uses fewer resources than Hadoop and has a simpler programming model. \n\nDevelopers mainly highlight the speed of the product in terms of task execution compared to MapReduce. \n\nSpark is also known for its ease of use<\/b> and sophisticated analytics<\/b>. Indeed, it has easy-to-use APIs to work on large data sets.  \n\nIn addition, Spark has some versatility. It has software for processing data in streams, a graph processing system. It also allows you to develop applications in Java, Scala, Python and R in a simplified way as well as to perform SQL queries. \n\nThe analysis engine includes numerous high-level libraries that support SQL queries, streaming data, machine learning and graph processing. These standard libraries allow developers to be more productive<\/b>. They can easily be combined in the same application to create complex workflows. \n\nFinally, spark achieves high performance for batch and streaming data with a DAG scheduler, a query optimizer and a physical execution engine.\n

The differences between Spark and MapReduce<\/h3>\nLet’s quickly define what MapReduce is:\n\nIt is a programming model launched by Google<\/b>. MapReduce allows the manipulation of large amounts of data. To process them, it distributes them in a cluster of machines. \n\nMapReduce<\/b> is very popular with companies with large data processing centers, such as Amazon or Facebook. Various frameworks have been created to implement it. The best known is Hadoop, developed by Apache Software Foundation<\/a><\/strong>.\n\nMoreover, with MapReduce<\/b>\"Data\n\nThus, Spark supports In-memory processing, which increases the performance of Big-Data analytics<\/b> applications and thus increases speed. It performs all data analysis operations in memory in real time and relies on disks only when memory is not sufficient. In contrast, Hadoop writes directly to disks<\/strong> after each operation and works in stages.\n

Who uses Spark?<\/h3>\nSince its release, the unified analytics engine has seen rapid adoption by companies in various industries. Internet stalwarts such as Netflix, Yahoo and eBay have developed Spark on a massive scale. \n\nCurrently, Spark has more than 1200 contributors such as Intel, Facebook, IBM… and is now the most important community in the world of Big Data<\/b>. \n\nIt allows unifying all spark Big Data applications. Spark is also suitable for real-time marketing campaigns, online product recommendations or cybersecurity.\n

What are the different tools in Spark?<\/h3>\n