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Warden - Big Data Glossary

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To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visualization tools. Descriptions are based on first-hand experience with these tools in a production environment.
Abstract: To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visualization tools. Descriptions are based on firsthand experience with these tools in a production environment. Read more...

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Big Data Glossary
Pete Warden
Published by OReilly Media

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Preface

Theres been a massive amount of innovation in data tools over the last few years, thanks to a few key trends:

Learning from the Web

Techniques originally developed by website developers coping with scaling issues are increasingly being applied to other domains.

CS+?=$$$

Google has proven that research techniques from computer science can be effective at solving problems and creating value in many real-world situations. Thats led to increased interest in cross-pollination and investment in academic research from commercial organizations.

Cheap hardware

Now that machines with a decent amount of processing power can be hired for just a few cents an hour, many more people can afford to do large-scale data processing. They cant afford the traditional high prices of professional data software, though, so theyve turned to open source alternatives.

These trends have led to a Cambrian explosion of new tools, which means that when youre planning a new data project, you have a lot to choose from. This guide aims to help you make those choices by describing each tool from the perspective of a developer looking to use it in an application. Wherever possible, this will be from my firsthand experiences or from those of colleagues who have used the systems in production environments. Ive made a deliberate choice to include my own opinions and impressions, so you should see this guide as a starting point for exploring the tools, not the final word. Ill do my best to explain what I like about each service, but your tastes and requirements may well be quite different.

Since the goal is to help experienced engineers navigate the new data landscape, this guide only covers tools that have been created or risen to prominence in the last few years. For example, Postgres is not covered because its been widely used for over a decade, but its Greenplum derivative is newer and less well-known, so it is included.

Conventions Used in This Book

The following typographical conventions are used in this book:

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Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.

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Using Code Examples

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Chapter 1. Terms

These new tools need some shorthand labels to describe their properties, and since theyre likely to be unfamiliar to traditional database users, Ill start off with a few definitions.

Document-Oriented

In a traditional relational database, the user begins by specifying a series of column types and names for a table. Information is then added as rows of values, with each of those named columns as a cell of each row. You cant have additional values that werent specified when you created the table, and every value must be present, even if its as a NULL value.

Document stores instead let you enter each record as a series of names with associated values, which you can picture being like a JavaScript object, a Python dictionary, or a Ruby hash. You dont specify ahead of time what names will be in each table using a schema. In theory, each record could contain a completely different set of named values, though in practice, the application layer often relies on an informal schema, with the client code expecting certain named values to be present.

The key advantage of this document-oriented approach is its flexibility. You can add or remove the equivalent of columns with no penalty, as long as the application layer doesnt rely on the values that were removed. A good analogy is the difference between languages where you declare the types of variables ahead of time, and those where the type is inferred by the compiler or interpreter. You lose information that can be used to automatically check correctness and optimize for performance, but it becomes a lot easier to prototype and experiment.

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