Digitalization made big data an integral part of many industries. To process large amounts of information easier, big data frameworks are used. However, choosing the right one is a challenge, as there are so many options in the market. Moreover, each business has its own needs.

Out of many big data frameworks, Spark and Hadoop MapReduce are considered among the best. In 2023, they are still in high demand, being praised by the users. Spark and Hadoop are favored by developers, as they allow distributed processing of the huge amounts of data.

This guide will tell you everything you need to know about the difference between MapReduce and Spark, their similarities, and the benefits of each framework. If you are interested, keep on reading!

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What is Hadoop MapReduce?

Before comparing Spark and Hadoop MapReduce, it’s important to learn more about each of these big data tools. Let’s start with Hadoop MapReduce.

Apache Hadoop is a collection of open source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. Hadoop MapReduce is a software framework for easily writing applications that process vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. MapReduce was developed in 2004 by Jeffery Dean and Sanjay Ghemawat, while Hadoop was created in 2005 Doug Cutting and Mike Carafella.

The name of MapReduce reflects its two main tasks. MapReduce is well-suited for iterative computations involving large amounts of data that necessitate parallel data processing. It uses a parallel algorithm on Hadoop cluster to process data sets.

Hadoop MapReduce is used for data analysis, fraud detection, genetic algorithms, resource planning, scheduling problems, etc. It is popular among e-commerce companies such as Amazon, Walmart, and eBay. With the help of Hadoop, these companies analyze buying behavior. Also, Mapreduce is used to evaluate information on social media platforms, such as Linkedin, Facebook, and Twitter. Entertainment companies, such as Netflix, also choose Hadoop to analyze the online behavior of their customers.

What is Apache Spark?

Apache Spark is an open-source, distributed processing system used for big data workloads. It was founded in 2009 at UC Berkeley AMPLab. It started out as a research project, and it became an open source tool in 2010.

Spark focuses on machine learning, real-time workloads and interactive query. It is capable of distributing data processing tasks across multiple computers. Apache Spark is often used for processing streaming data and fog computing.

Apache Spark is in high demand in the market. More than 1000 organizations use this framework in their production, some of them are mentioned here.

Many industries choose to work with Spark. Here is how each of them uses this framework:

  • The banking industry uses Spark to recommend new financial products and predict future trends.
  • In healthcare, Spark is essential for recommending patient treatment and building comprehensive patient care.
  • Also, Apache Spark is used in manufacturing: it recommends the right time to do preventive maintenance and eliminates the downtime of internet-connected equipment.
  • The retail industry chooses Spark to attract and keep customers by leveraging the power of big data analytics. Retailers also use Apache Spark to analyze customer data for identifying patterns in their behavior.

Now let’s proceed to Spark vs Hadoop MapReduce comparison.

Comparing Spark and Hadoop MapReduce: Differences

Comparing Spark and Hadoop MapReduce

Before choosing between Spark and Hadoop MapReduce, it’s important to consider what sets these data processing frameworks apart. Each of these tools has its pros and cons. Now it’s time to discover the difference between Spark and Hadoop MapReduce.

Spark vs MapReduce: Performance

The first thing you should pay attention to is the frameworks’ performances. Hadoop MapReduce persists data back to the disc after a map or reduce operation, while Apache Spark persists data in RAM, or random access memory. For smaller workloads, Spark’s data processing speeds are up to 100x faster than MapReduce. The data you use for Hadoop isn’t meant to fit in the memory, so other services can coexist with MapReduce.

Spark vs Hadoop MapReduce: Ease of use

One of the main benefits of Spark is that it has pre-built APIs for Python, Scala and Java. Spark has simple building blocks, that’s why it’s easier to write user-defined functions. Using Hadoop, on the other hand, is more challenging. MapReduce doesn’t have an interactive mode, while Spark does. However, there are a couple of tools to make Hadoop MapReduce easier to use, for instance, Apache Impala or Apache Tez.

Hadoop MapReduce vs Spark: Data processing

Spark is good at real-time processing and batch processing. Thus, you don’t have to split tasks across multiple platforms. It uses in-memory caching and optimized query execution to provide quick results when running queries against any amount of data. Hadoop MapReduce, on the other hand, is more suitable for batch processing. By dividing petabytes of data into smaller chunks and working them in parallel on Hadoop commodity computers, MapReduce makes concurrent processing easier.

Spark vs MapReduce: Security

MapReduce is more advanced when it comes to security in comparison with Spark. It’s because security in Spark is by default turned off, so it cannot deal with possible attacks. Spark enhances security with authentication via shared secret or event logging, whereas Hadoop uses multiple authentications and access control methods. Though overall, Hadoop is more secure, Spark can integrate with Hadoop to reach a higher security level. Hadoop, however, has its default security benefits and also can be integrated with Hadoop security projects. Apache Hadoop MapReduce is more secure because of Kerberos and it also supports Access Control Lists (ACLs) which are a traditional file permission model.

Similarities between Spark and MapReduce

As you can see, Spark and MapReduce are both popular nowadays. Of course, there are things that both of these frameworks share. Before choosing what is better, Spark or Hadoop MapReduce, let’s analyze their common features.

Hadoop MapReduce vs Spark: Cost

MapReduce and Spark are both open-source solutions, and such software typically is free. However, you still have to spend money on staff and machines. One more common feature of Spark and MapReduce is that both of these frameworks can run on the cloud and use commodity servers. But if you need to process large amounts of data, Hadoop will be more cost-effective, because hard disk space is cheaper than memory space.

Apache Spark vs Hadoop MapReduce: Compatibility

Both Spark and Hadoop are compatible with different types of data and data sources. Moreover, Spark can run in the cloud or be a standalone application. The same file formats and types of data can be integrated with Hadoop and Spark, because data sources that use the Hadoop Input format are supported by Spark.

Hadoop MapReduce vs Apache Spark: Failure Tolerance

One more thing that unites Spark and Hadoop MapReduce is that they both have retries per task and speculative execution. Still, Spark relies on RAM and MapReduce relies on hard drives more. It gives MapReduce an additional advantage because it’s more secure in case something goes wrong during the execution. If the process stops in the middle, you can continue from where you left.

When Does Using MapReduce Make Sense?

If you gravitate towards Hadoop MapReduce, you should know the cases where it is the most efficient. If the speed of data processing is not essential to you, then you should consider Hadoop. It is good for the linear processing of large sets of data. A large chunk of data is divided into smaller ones, but at the end you get a single result. Hadoop MapReduce can outperform Spark if the resulting dataset is bigger than available random access memory.

Let’s not forget that using Hadoop MapReduce is a good economical solution. The data processing can be executed at night if there is no critical need for fast processing. Such execution can save you time and money. Hadoop MapReduce is also suitable for large-scale graph analysis.

When Choose Apache Spark?

Contrary to Hadoop MapReduce, Spark guarantees you fast data processing thanks to in-memory processing. If the data needs to be processed repeatedly, Spark is a better option. Resilient Distributed Datasets (RDDs) in Spark allow for numerous map operations to be performed in memory. Using Spark allows you to get immediate insights due to its nearly real-time processing.

Spark has a built-in machine-learning library, and it’s also really convenient for developers. At the same time, Hadoop needs a third-party library. Instead of having a Big Data tool for each type of job, if your project, product, or service needs both batch and real-time processing, you can do it with Apache Spark and its libraries. Also, Spark is good for joining datasets, as it creates combinations faster than Hadoop MapReduce.

Hadoop MapReduce & Spark

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This guide shows that both Spark and Hadoop have something unique to offer. Contact us and choose the ultimate software framework for you!

Frequently Asked Questions

When to use MapReduce vs Spark?

Comparing Spark and MapReduce, Spark is the better choice when you need fast processing or when data needs to be processed more than once. MapReduce is more suitable for processing large amounts of data, and it is a good economical solution.

Why is Spark faster than MapReduce?

Spark and MapReduce vary primarily in that Spark processes data in memory and keeps it there for following steps while MapReduce processes data on storage. As a result, Spark's data processing speeds are up to 100 times quicker than MapReduce's for lesser workloads.

What is the main difference between MapReduce and Spark?

MapReduce is a programming model developed by Google for processing large amounts of data in parallel across multiple nodes in a cluster. Spark, on the other hand, is a general-purpose distributed computing framework designed to be faster and more flexible than MapReduce.