THE GROWTH OF USER-DRIVEN CONTENT has fueled a rapid increase in the volume and type of data that is generated, manipulated, analyzed, and archived. In addition, varied newer sets of sources, including sensors, Global Positioning Systems (GPS), automated trackers and monitoring systems, are generating a lot of data. These larger volumes of data sets, often termed big data, are imposing newer challenges and opportunities around storage, analysis, and archival.
In parallel to the fast data growth, data is also becoming increasingly semi-structured and sparse. This means the traditional data management techniques around upfront schema definition and relational references is also being questioned.
The quest to solve the problems related to large-volume and semi-structured data has led to the emergence of a class of newer types of database products. This new class of database products consists of column-oriented data stores, key/value pair databases, and document databases. Collectively, these are identified as NoSQL.
The products that fall under the NoSQL umbrella are quite varied, each with their unique sets of features and value propositions. Given this, it often becomes difficult to decide which product to use for the case at hand. This book prepares you to understand the entire NoSQL landscape. It provides the essential concepts that act as the building blocks for many of the NoSQL products. Instead of covering a single product exhaustively, it provides a fair coverage ...