Essential Guide

This Essential Guide is a collection of articles, videos and other content selected by our editors to give you a comprehensive view of this topic.

Maximizing and managing big data with SOA middleware

Managing big data is going to become an increasingly important task for technology professionals. Research shows a growing number of organizations are tackling big data head-on. The notorious "three V's" -- volume, variety and velocity -- can cause trouble for even those with years of experience. One of the best ways to beat big data obstacles is to be well informed.

This guide brings together a range of articles that offer expert advice on managing big data, large data-set trends and key terms anyone preparing to work with big data will need to be familiar with.

Are you ready to manage big data? When you've finished reading this guide, see how much information you've absorbed by taking our short quiz.

Strategies and techniques

1. Tools and best practices for managing big data

Managing big data is no small task. Fortunately, there are tools and best practices that can be followed in order to make handling large data sets easier. Read on for advice to successfully take care of vast quantities of information.

Problem solving

2. Areas that struggle with large volume sets

Big data is notorious for giving technology professionals headaches. There are ways to finagle the technology. This section features articles that highlight specific examples on how large forms of data can be tamed.

  • How to best use SOA for big data management

    Big data and cloud data management can be problematic. Learn how SOA can manage data in several ways.


  • Pushing big data to new limits

    Content management systems that need to sift through huge amounts of data are big data problems in need of a solution. Fortunately, projects like Hadoop and MapReduce are coming to the rescue.


  • Solving scalability issues with big data

    The highly centralized enterprise data center is becoming a thing of the past, as organizations must embrace a more distributed model to deal with everything from content management to big data. Here we examine how technologies like Hadoop and NoSQL fit into modern distributed architectures in a way that solves scalability and performance problems.


  • Reducing complexity with patterns for big data design

    Design patterns have caught on as a way to simplify development of software applications. Increasingly, that means using them for big data design.


  • Solving CMS woes with big-data solutions

    With the proliferation of mobile devices and embedded devices, all acquiring and delivering data to various sources and repositories, organizations that manage content are being overwhelmed. How can organizations not only aggregate this data, but filter it and discover what data is meaningful and what is not? It's a big-data problem that requires a big-data solution.



3. What lies ahead for big data

There is a lot in store for big data. From more organizations planning to take on big data to how large volume sets affect architects, there is a lot of information to be privy to. Peruse this section to see what experts are forecasting for the future of working with large volumes of data.


4. Must-know big data terminology

If you are just getting started with managing big data, this glossary provides common terms you'll need to know.


5. What is your big data IQ?

How much do you really know about managing big data? Prove your knowledge with our short quiz!

Big data 101 quiz