Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 Min Read
    big data analytics in transporation
    Turning Data Into Decisions: How Analytics Improves Transportation Strategy
    3 Min Read
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Demystifying Hadoop: Not All Problems Are Hadoop-able
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Demystifying Hadoop: Not All Problems Are Hadoop-able
Big Data

Demystifying Hadoop: Not All Problems Are Hadoop-able

RadhikaAtEmcien
RadhikaAtEmcien
6 Min Read
hadoop big data
SHARE

When you hear about Big Data, Hadoop hype follows almost automatically. But people often ask me what Hadoop actually does. Built by Yahoo and Google to essentially index the Internet, Hadoop is not a data warehouse or storage solution: it’s a tool that’s useful when information can be broken up, analyzed in pieces and put back together. 

hadoop big data

When you hear about Big Data, Hadoop hype follows almost automatically. But people often ask me what Hadoop actually does. Built by Yahoo and Google to essentially index the Internet, Hadoop is not a data warehouse or storage solution: it’s a tool that’s useful when information can be broken up, analyzed in pieces and put back together. 

hadoop big data

More Read

LinkedIn and Hiring: Dream. Fit. Passion.
New Report Shows Big Data Plays Key Role In Improving Driver Safety
US computer scientists have found that random networks – the…
How Data and IoT Tech is Driving Business Processes Today
Book: SAS for Dummies

For example, if a chain of convenience stores needs to find out how many customers used MasterCard, Visa, American Express, or cash at the pump in the past year, they can use Hadoop as a tool to retrieve that information because it can be divided up and managed in pieces per location, without affecting the big picture.

However, if you’re working with data that requires an examination of the relationships and dependencies within the data, you can’t just look at it in pieces and get the “big picture” of what the data is trying to tell you. So, back to the previous example, this approach would fail if the chain wants to know what food and beverages are being purchased together in rural vs. urban locations and how weather impacts those buying patterns.

The hype around Hadoop makes it seem like a one-size-fits-all solution for leveraging big data, but the reality is that not all problems are Hadoop-able, and more and more business users are learning that. Jaikumar Vijayan of ComputerWorld wrote, “Hadoop isn’t enough anymore for enterprises that need new and faster ways to extract business value from massive datasets.”

Time is a major factor, but what about requiring an IT army to run Hadoop? Steve Rosenbush wrote in Wall Street Journal about how GameStop CIO Jeff Donaldson “picked a more traditional approach for analyzing large amounts of customer data, because he didn’t want to manage the complexity of having his engineers learn Hadoop, or have to call in consultants for help.”

While Hadoop is an effective and low-cost tool for some companies, it is not an application and does not get business users any closer to the most critical part of Big Data: getting to the insight. Hadoop chops and dices and stores, but does not make a consumable dish! It leaves users wanting for the value of big data, some of which include:

  • Law enforcement and intelligence agencies seeking insight from their data to mitigate threats to public safety.
  • Healthcare institutions trying to predict disease outbreaks or customize treatments to diseases.
  • Retailers wanting insight into demand trends and customer-buying patterns to serve the markets more profitably
  • Supply chain professionals wanting insight into data to understand the cause-and effect across the nodes on the chain.

All these business problems require the ability to extract insight from data. Rather than break the data into pieces and store-n-query, organizations need the ability to detect patterns and gain insights from their data. Hadoop destroys the naturally occurring patterns and connections because its functionality is based on breaking up data. The problem is that most organizations don’t know that their data can be represented as a graph nor the possibilities that come with leveraging connections within the data.

Take healthcare, for example. You have nodes for people, medicine, symptoms and side effects. To determine the type of person most likely to have the least side effects related to a certain medication, one needs to leverage the patterns of connections within data – as opposed to breaking apart those connections into disparate clusters. This is the kind of analysis that is not going to lend itself to being partitioned in a Hadoop-able way.

Law enforcement agencies have data comprised of people, organizations, places and words. These connection points, if not Hadoop-ed, can reveal valuable connections to networks of interest and key influencers within those networks. But the value lies in the data that’s sparse, so it needs to be assessed all at the same time instead of as distributed fragments.

Though Hadoop is getting a lot of attention, it may not always be the best approach to crunching Big Data for strategic insights. “[Hadoop] is simply too slow for companies that need sub-millisecond query response times,” wrote Vijayan. Whether it’s healthcare, manufacturing, retail or banking, companies with data that can be represented as a “big picture” demand effective solutions beyond the methods they are likely using now such as spreadsheets made by guesstimates and intuition.

TAGGED:business intelligencehadoopleverage
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Why the AI Race Is Being Decided at the Dataset Level
Why the AI Race Is Being Decided at the Dataset Level
Artificial Intelligence Big Data Exclusive
image fx (60)
Data Analytics Driving the Modern E-commerce Warehouse
Analytics Big Data Exclusive
ai for building crypto banks
Building Your Own Crypto Bank with AI
Blockchain Exclusive
julia taubitz vn5s g5spky unsplash
Benefits of AI in Nursing Education Amid Medicaid Cuts
Artificial Intelligence Exclusive News

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Persuasion in simple terms

3 Min Read
big data scientist skills
AnalyticsBig DataHadoopMapReduce

Is Hadoop Knowledge a Must-Have for Today’s Big Data Scientist?

3 Min Read

Dresner: Mobile Business Intelligence to Transform BI Industry

7 Min Read

Thinking different with decision analysis

4 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?