Big data are datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing. This trend continues because of the benefits of working with larger and larger datasets allowing analysts to "spot business trends, prevent diseases, combat crime." Though a moving target, current limits are on the order of terabytes, exabytes and zettabytes of data. Scientists regularly encounter this problem in meteorology, genomics , connectomics, complex physics simulations, biological and environmental research, Internet search, finance and business informatics. Data sets also grow in size because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing)"software logs, cameras, microphones, RFID readers, wireless sensor networks and so on."One current feature of big data is the difficulty working with it using relational databases and desktop statistics/visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers." The size of "Big data" varies depending on the capabilities of the organization managing the set. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."This book is your ultimate resource for Big Data. Here you will find the most up-to-date information, analysis, background and everything you need to know. In easy to read chapters, with extensive references and links to get you to know all there is to know about Big Data right away, covering: Big data, BigTable, Cloud computing, Data assimilation, Database theory, Database-centric architecture, Data Intensive Computing, Data structure, ECL, data-centric programming language for Big Data, Apache Hadoop, HPCC, MapReduce, Online database, Real-time database, Relational database, Social data revolution, IBM Scale-out File Services, Supercomputer, Teradata, Comparison of database tools, Comparison of object-relational database management systems, ACID, ANSI-SPARC Architecture, Armstrong's axioms, Associative model of data, AutoNumber, Bidirectionalization, Bitemporal data, Block contention, Candidate key, Citrusleaf database, Column-oriented DBMS, Commit (data management), Comparison of relational database management systems, Connection pool, Content repository API for Java, Correlation database, Create, read, update and delete, Cursor (databases), Data Control Language, Data Definition Language, Data Manipulation Language, Data mart, Data masking, Data redundancy, Data retrieval, Data store, Database, Database administration and automation, Database design, Database dump, Database engine, Database management system, Database model, Database normalization, Database storage structures, Database system, Database transaction, Database trigger, Database tuning, Datasource, Deductive database, Distributed database management system, Document-oriented database, Enterprise database management, Expression index, Federated database system, Foreign key, Formatted File System, Heterogeneous Database System, Hierarchical query, In-database processing, In-memory database, Index (database), Integrated Data Management, ISBL, Least number bits, Life cycle of a relational database, List of object database management systems, List of relational database management systems, Mariposa (database), Master data management, Metadatabase, Microsoft Access, MultiValue...and much moreThis book explains in-depth the real drivers and workings of Big Data. It reduces the risk of your technology, time and resources investment decisions by enabling you to compare your understanding of Big Data with the objectivity of experienced professionals.