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Hadoop vs. Spark: Which One Is Right for You?

In the big data universe, Hadoop and Spark are often pitted against one another as direct competitors. When deciding which of these two frameworks is right for your organization, it’s important to be aware of their essential differences and similarities. Both have many of the same uses, but they use markedly different approaches to solving big data problems. Because of these differences, there are times when it might be recommended to use one versus the other.

Basic Differences

Hadoop is an open-source project by the Apache Software Foundation that provides a software library and framework for processing large data sets. This processing is typically done on a distributed computing platform that spans a cluster of computing devices. Hadoop was conceived in response to the inability of standard software and databases to process this “big data” quickly enough to meet the needs of most organizations.

A key feature of Hadoop is the Hadoop Distributed File System (HDFS), which is designed to store very large data sets reliably, and to stream this data at high bandwidth to user applications. One such application is MapReduce, Hadoop’s default programming model for transformation and analysis of the data. Basically, HDFS takes care of data storage, and MapReduce performs the data analytics.

Spark – also developed by Apache – is a big data processing engine that, like Hadoop MapReduce, is designed to run on a cluster computing framework. A key difference is it performs and stores as many data operations as possible in-memory, whereas MapReduce does a disk read/write for every operation.

Unlike Hadoop, Spark doesn’t come with its own distributed file system, so it requires a third-party one. Often, Spark users will use HDFS for this purpose. When people are discussing Hadoop in comparison to Spark, they’ll often be referring to MapReduce.

Performance

The first thing usually mentioned when comparing Hadoop’s MapReduce to Spark is how much faster Spark is. This is due to Spark’s in-memory processing, which according to its makers can make it up to 100 times faster in memory, or 10 times faster on disk, than the strictly disk-based MapReduce.

This speed advantage allows Spark to deliver near real-time analytics to the user. This makes it ideal for processing data from sources such as marketing campaigns, social media, Internet of Things devices and cybersecurity programs. Spark even comes with its own machine learning library, making it a popular choice for data scientists working in this domain.

MapReduce, on the other hand, is more suited to batch-processing jobs where timing isn’t so critical. It was originally developed by Google to process web data for its search engine in a highly parallel fashion. It remains useful for business intelligence (BI) and business analytics, where large volumes of historical data need to be processed for reports or visualizations.

Read More: What is Business Intelligence?

Fault Tolerance

Both Hadoop and Spark have built-in safeguards against data loss. With Hadoop MapReduce, data is constantly being saved to disk. The HDFS storage system provides additional fault tolerance by replicating data across the cluster. An application using HDFS can specify how many replicas of a file are created.

Spark stores its data in Resilient Distributed Datasets (RDDs), which can be written to either memory or disk. If any partition of an RDD is lost, it will beautomatically recomputed using the data transformations that originally created it.

Costs

Being open-source software, both the Hadoop and Spark frameworks are free to use. Because RAM is much more expensive than disk storage, individual Spark machines tend to cost more than Hadoop ones. However, Spark usually requires fewer machines to run, due to the need to have many disk I/O channels available to do effective big data analytics with Hadoop.

Choosing the Right Framework

Both Hadoop and Spark have important roles to play in big data. Hadoop provides a distributed file system that Spark can use for long-term data storage. Spark’s in-memory processing is ideal for applications that need to produce real-time results, such as performance dashboards or product recommendations. On the other hand, if you simply need to crunch a huge amount of structured data for later use, there is likely no need for the advanced streaming analytics and machine learning functionality of Spark.

Many organizations will find useful features embedded within both Hadoop and Spark. If you are planning to study data science, or go all the way with a data science degree, being aware of both will be a huge advantage.

Learn more about data science education options – and if you’re already sure you want to pursue an advanced degree, check out our favorite online degree programs.