As you make your way in this crazy, tech-encrusted landscape, you’ve likely heard some of the standard arguments. And within the sea of comparisons and competing products, the argument and agreement around Hadoop vs. Spark has gained increasing importance. Understanding the Spark vs. Hadoop debate will help you get a grasp on your career and guide its development. It can be confusing, but it’s worth working through the details to get a real understanding of the issue.
This article is your guiding light and will help you work your way through the Apache Spark vs. Hadoop debate. We show you the key similarities and differences between the products and help you to figure out which one would work best with your business. Both products are excellent, and the debate around which one to use is ongoing. By the time you finish this article, you’ll be ready to add to the conversation.
What Are the Similarities?
One of the features of most great tech jobs is an open-minded atmosphere that encourages exploration and development. The same approach works well when comparing products such as Hadoop and Spark. Either option offers a quality framework with robust features. So, what are Spark and Hadoop, anyway? What options and approaches do the two share, and are there areas in which they offer identical service?
The key concept to understand when working with Spark and Hadoop is the notion of big data. Big data is the collection and examination of massive amounts of information with the goal of better understanding the market, clients, and needs. Big data produces overwhelming amounts of info, though, and you need to use specialized distribution and analysis methods to handle it. Both Hadoop and Spark do this through distributed environments of computers and apps.
Processing Power and Speed
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Okay, so the two frameworks share some big similarities. That’s all well and good, but we’re here for the fun stuff. If you’ve spent time in tech, you’re aware that most products have a niche, and in that niche, they’re the best at what they do. Hadoop and Spark are no different, and each has an area in which they shine. One area where Spark shines is in processing power and speed.
Hadoop uses a MapReduce algorithm that needs to read and write from a disk, which slows operations significantly. Processor operations in Hadoop also run slowly to prevent issues while handling the large datasets big data operations produce. Spark is newer and is a much faster entity—it uses cluster computing to extend the MapReduce model and significantly increase processing speed. And because Spark uses RAM instead of disk space, it’s about a hundred times faster than Hadoop when moving data.
Batch Processing vs. Real-Time Data
Spark and Hadoop come from different eras of computer design and development, and it shows in the manner in which they handle data. Hadoop has to manage its data in batches thanks to its version of MapReduce, and that means it has no ability to deal with real-time data as it arrives. This is both an advantage and a disadvantage—batch processing is an efficient method of dealing with large amounts of data, but the lack of a method to handle streaming data reduces Hadoop’s performance.
Spark is a much quicker framework overall. It can handle batch processing, but it also has well-designed streaming data processing that can handle a flood of incoming information and still keep its head above water. Spark can multitask, too; it can perform streaming and batch processing in the same cluster without issue. It can even add machine learning and other features to the cluster without impacting performance.
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