Five things you need to know about Hadoop vs Apache Spark

They're sometimes viewed as competitors in the big-data space, but the growing consensus is that they're better together

Listen in on any conversation about big data, and you'll probably hear mention of Hadoop or Apache Spark. Here's a brief look at what they do and how they compare.

1: They do different things. Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes. Hadoop is essentially a distributed data infrastructure: It distributes massive data collections across multiple nodes within a cluster of commodity servers, which means you don't need to buy and maintain expensive custom hardware. It also indexes and keeps track of that data, enabling big-data processing and analytics far more effectively than was possible previously. Spark, on the other hand, is a data-processing tool that operates on those distributed data collections; it doesn't do distributed storage.

2: You can use one without the other. Hadoop includes not just a storage component, known as the Hadoop Distributed File System, but also a processing component called MapReduce, so you don't need Spark to get your processing done. Conversely, you can also use Spark without Hadoop. Spark does not come with its own file management system, though, so it needs to be integrated with one -- if not HDFS, then another cloud-based data platform. Spark was designed for Hadoop, however, so many agree they're better together.

3: Spark is speedier. Spark is generally a lot faster than MapReduce because of the way it processes data. While MapReduce operates in steps, Spark operates on the whole data set in one fell swoop. "The MapReduce workflow looks like this: read data from the cluster, perform an operation, write results to the cluster, read updated data from the cluster, perform next operation, write next results to the cluster, etc.," explained Kirk Borne, principal data scientist at Booz Allen Hamilton. Spark, on the other hand, completes the full data analytics operations in-memory and in near real-time: "Read data from the cluster, perform all of the requisite analytic operations, write results to the cluster, done," Borne said. Spark can be as much as 10 times faster than MapReduce for batch processing and up to 100 times faster for in-memory analytics, he said.

4: You may not need Spark's speed. MapReduce's processing style can be just fine if your data operations and reporting requirements are mostly static and you can wait for batch-mode processing. But if you need to do analytics on streaming data, like from sensors on a factory floor, or have applications that require multiple operations, you probably want to go with Spark. Most machine-learning algorithms, for example, require multiple operations. Common applications for Spark include real-time marketing campaigns, online product recommendations, cybersecurity analytics and machine log monitoring.

5: Failure recovery: different, but still good. Hadoop is naturally resilient to system faults or failures since data are written to disk after every operation, but Spark has similar built-in resiliency by virtue of the fact that its data objects are stored in something called resilient distributed datasets distributed across the data cluster. "These data objects can be stored in memory or on disks, and RDD provides full recovery from faults or failures," Borne pointed out.

Join the newsletter!

Or

Sign up to gain exclusive access to email subscriptions, event invitations, competitions, giveaways, and much more.

Membership is free, and your security and privacy remain protected. View our privacy policy before signing up.

Error: Please check your email address.
Keep up with the latest tech news, reviews and previews by subscribing to the Good Gear Guide newsletter.

Katherine Noyes

IDG News Service
Show Comments

Cool Tech

Bang and Olufsen Beosound Stage - Dolby Atmos Soundbar

Learn more >

Toys for Boys

Sony WF-1000XM3 Wireless Noise Cancelling Headphones

Learn more >

Nakamichi Delta 100 3-Way Hi Fi Speaker System

Learn more >

ASUS ROG, ACRONYM partner for Special Edition Zephyrus G14

Learn more >

Family Friendly

Philips Sonicare Diamond Clean 9000 Toothbrush

Learn more >

Mario Kart Live: Home Circuit for Nintendo Switch

Learn more >

Stocking Stuffer

SunnyBunny Snowflakes 20 LED Solar Powered Fairy String

Learn more >

Teac 7 inch Swivel Screen Portable DVD Player

Learn more >

Christmas Gift Guide

Click for more ›

Most Popular Reviews

Latest Articles

Resources

PCW Evaluation Team

Tom Pope

Dynabook Portégé X30L-G

Ultimately this laptop has achieved everything I would hope for in a laptop for work, while fitting that into a form factor and weight that is remarkable.

Tom Sellers

MSI P65

This smart laptop was enjoyable to use and great to work on – creating content was super simple.

Lolita Wang

MSI GT76

It really doesn’t get more “gaming laptop” than this.

Jack Jeffries

MSI GS75

As the Maserati or BMW of laptops, it would fit perfectly in the hands of a professional needing firepower under the hood, sophistication and class on the surface, and gaming prowess (sports mode if you will) in between.

Taylor Carr

MSI PS63

The MSI PS63 is an amazing laptop and I would definitely consider buying one in the future.

Christopher Low

Brother RJ-4230B

This small mobile printer is exactly what I need for invoicing and other jobs such as sending fellow tradesman details or step-by-step instructions that I can easily print off from my phone or the Web.

Featured Content

Don’t have an account? Sign up here

Don't have an account? Sign up now

Forgot password?