Big Data requires very specific tools. Without them, your ability to work with massive troves of data will be greatly hindered. Given how every single business across the globe depends on data to remain competitive, it’s imperative that your company knows (and uses) the right tools for the job.
You might think such a decision would be limited to choosing the right database for the task. Although that is one of the most important choices you’ll need to make, it won’t be the last. In fact, several tools are required to successfully venture into the realm of big data.
Two such tools are Spark and MapReduce. What are these tools and what is the difference between them? These are important questions you must ask and answer. Fortunately, we’re here to help facilitate the answering of that looming question, “What is the difference between Spark and MapReduce?” Both of these tools are frameworks that have become absolutely crucial for many companies who depend on Big Data, but they are fundamentally different.
Let’s dive in and see just what the difference is between these two frameworks. We’ll look at this through the lens of 5 different categories: Data Processing, Failure Recovery, Operability, Performance, and Security. Before we address those issues, let’s first find out what these 2 tools are.
What is Spark?
Spark is an open-source, general-purpose, unified analytics engine used for processing massive amounts of data. The Spark core database processing engine works with libraries for SQL machine learning, graph computation, and stream processing.
Spark is supported by Java, Python, Scala, and R and is used by app developers and data scientists to rapidly query, analyze, and transform data at scale. Spark is often used for ETL and SQL batch jobs across massive data sets, the processing of streaming data from IoT devices, various types of sensors, and financial systems, as well as machine learning.
What is MapReduce?
MapReduce is a programming model/pattern within the Apache Hadoop framework, used to access massive data stores in the Hadoop File System (HDFS), which makes it a core function of Hadoop.
MapReduce makes concurrent processing possible by splitting massive data sets into smaller chunks and processing them in parallel on Hadoop servers to aggregate data from a cluster and return the output to an application.
Both Spark and MapReduce are outstanding at processing different types of data. The biggest difference between the two, however, is that Spark includes nearly everything you need for your data processing needs, while MapReduce really only excels at batch processing (where it happens to be the best on the market).
So, if you’re looking for a Swiss Army Knife of data processing, Spark is what you want. If, on the other hand, you want serious batch processing power, MapReduce is your tool.
This is an area where the two are quite different. Spark does all of its data processing in RAM, which makes it very fast but less than adept at failure recovery. Should Spark experience a failure, data recovery is considerably more challenging because data is processed in volatile memory.
MapReduce, on the other hand, handles data processing in a more standard fashion (on local storage). This means that if MapReduce encounters a failure, it can pick up where it left off once it is back online.
In other words, if recovering from a failure (such as a power loss), MapReduce is the best way to go.
Simply put, Spark is far easier to program than MapReduce. Not only is Spark interactive (so developers can run commands and get immediate feedback), it includes building blocks to simplify the development process. You’ll also find built-in APIs for Python, Java, and Scala.
MapReduce, on the other hand, is considerably more challenging to develop with. There is no interactive mode, nor does it have built-in APIs. To get the most out of MapReduce, your developers might have to lean on third-party tools to help with the process.
If performance is at the top of your list, then Spark is the way to go. Because it processes data in memory (RAM) instead of on slower local storage, the difference between the two is considerable (with Spark being up to 100 times faster than MapReduce).
The one caveat to that is, due to the nature of in-memory processing, should you lose power to a server, you will lose data. However, if you need to squeeze out as much speed as possible, you can’t go wrong with Spark.
This one is pretty straightforward. When working with Spark, you’ll find far fewer security tools and features, which could cause your data to be vulnerable. And although there are methods to better secure Spark (such as Kerberos authentication), it’s not exactly an easy process.
On the other hand, both Knox Gateway and Apache Sentry are readily available to MapReduce to help make the platform considerably more secure. Although it does take effort to secure both Spark and MapReduce, you’ll find the latter more secure “out of the box.”
To make the choice simple: If you want speed, you want Spark. If you want reliability, you want MapReduce. It really can be viewed through such a basic lens. Either way, you go, however, you’ll want to consider one of these tools if you’re serious about Big Data.