NoSQL For Dummies
eBook - ePub

NoSQL For Dummies

Adam Fowler

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eBook - ePub

NoSQL For Dummies

Adam Fowler

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About This Book

Get up to speed on the nuances of NoSQL databases and what they mean for your organization

This easy to read guide to NoSQL databases provides the type of no-nonsense overview and analysis that you need to learn, including what NoSQL is and which database is right for you. Featuring specific evaluation criteria for NoSQL databases, along with a look into the pros and cons of the most popular options, NoSQL For Dummies provides the fastest and easiest way to dive into the details of this incredible technology. You'll gain an understanding of how to use NoSQL databases for mission-critical enterprise architectures and projects, and real-world examples reinforce the primary points to create an action-oriented resource for IT pros.

If you're planning a big data project or platform, you probably already know you need to select a NoSQL database to complete your architecture. But with options flooding the market and updates and add-ons coming at a rapid pace, determining what you require now, and in the future, can be a tall task. This is where NoSQL For Dummies comes in!

  • Learn the basic tenets of NoSQL databases and why they have come to the forefront as data has outpaced the capabilities of relational databases
  • Discover major players among NoSQL databases, including Cassandra, MongoDB, MarkLogic, Neo4J, and others
  • Get an in-depth look at the benefits and disadvantages of the wide variety of NoSQL database options
  • Explore the needs of your organization as they relate to the capabilities of specific NoSQL databases

Big data and Hadoop get all the attention, but when it comes down to it, NoSQL databases are the engines that power many big data analytics initiatives. With NoSQL For Dummies, you'll go beyond relational databases to ramp up your enterprise's data architecture in no time.

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For Dummies
Part I

Getting Started with NoSQL

Visit for great Dummies content online.
In this part . . .
  • Discover exactly what NoSQL is.
  • Identifying terminology.
  • Categorizing technology.
  • Visit for great Dummies content online.
Chapter 1

Introducing NoSQL: The Big Picture

In This Chapter
Examining the past
Recognizing changes
Applying capabilities
The data landscape has changed. During the past 15 years, the explosion of the World Wide Web, social media, web forms you have to fill in, and greater connectivity to the Internet means that more than ever before a vast array of data is in use.
New and often crucial information is generated hourly, from simple tweets about what people have for dinner to critical medical notes by healthcare providers. As a result, systems designers no longer have the luxury of closeting themselves in a room for a couple of years designing systems to handle new data. Instead, they must quickly create systems that store data and make information readily available for search, consolidation, and analysis. All of this means that a particular kind of systems technology is needed.
The good news is that a huge array of these kinds of systems already exists in the form of NoSQL databases. The not-so-good news is that many people don’t understand what NoSQL databases do or why and how to use them. Not to worry, though. That’s why I wrote this book. In this chapter, I introduce you to NoSQL and help you understand why you need to consider this technology further now.

A Brief History of NoSQL

The perception of the term NoSQL has evolved since it was launched in 1998. So, in this section, I want to explain how NoSQL is currently defined, and then propose a more appropriate definition for it. I even cover NoSQL history background in the side bars.

Amazon and Google papers

NoSQL isn’t a single technology invented by a couple of guys in a garage or a mathematician theorizing about data structures. The concepts behind NoSQL developed slowly over several years. Independent groups then took those ideas and applied them to their own data problems, thereby creating the various NoSQL databases that exist today.

Google Bigtable paper

In 2006, Google released a paper that described its Bigtable distributed structured database. Google described Bigtable as follows: “Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers.”
Similar to an RDBMS model at first sight, Bigtable stores rows with a single key and stores data in the rows within related column families. Therefore, accessing all related data is as easy as retrieving a record by using an ID rather than a complex join, as in relational database SQL.
This model also means that distributing data is more straightforward than with relational databases. By using simple keys, related data — such as all pages on the same website (given as an example in Google’s paper) — can be grouped together, which increases the speed of analysis. You can think of Bigtable as an alternative to many tables with relationships. That is, with Bigtable, column families allow related data to be stored in a single record.
Bigtable is designed to be distributed on commodity servers, a common theme for all NoSQL databases created after the information explosion caused by the adoption of the World Wide Web. A commodity server is one without complex bells and whistles — for example, Dell or HP servers with perhaps 2 CPUs, 8 to 16 cores, and 32 to 96GB of RAM. Nothing fancy, lots of them, and cheaper than buying one big server (which is like putting all your eggs in one expensive basket).

Amazon Dynamo paper

Amazon released a paper of its own in 2007 describing its Dynamo data storage application. In Amazon’s words: “Dynamo is used to manage the state of services that have very high reliability requirements and need tight control over the tradeoffs between availability, consistency, cost-effectiveness and performance.”
The paper goes on the describe how a lot of Amazon data is stored by use of a primary key, how consistent hashing is used to partition and distribute data, and how object versioning is used to maintain consistency across data centers.
The Dynamo paper basically describes the first globally distributed key-value store used at Amazon. Here the keys are logical IDs, and the values can be any binary value of interest to the developer. A very simple model, indeed.
These two papers inspired many different organizations to create their NoSQL databases. There were so many variations that some people thought it necessary to meet and discuss the various approaches being taken (see “The second NoSQL ‘meetup’” sidebar).

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