pt. I. Getting started with big data -- 1. Grasping the fundamentals of big data -- Evolution of data management -- Understanding the waves of managing data -- Creating manageable data structures -- Web and content management -- Managing big data -- Defining big data -- Building a successful big data management architecture -- Capture, organize, integrate, analyze and act -- Architectural foundation -- Performance matters -- Traditional and advanced analytics -- 2. Examining big data types -- Defining structured data -- Exploring sources of big structured data -- Understanding the role of relational databases in big data -- Defining unstructured data -- Exploring sources of unstructured data -- Understanding the role of a CMS in big data management -- Looking at real-time and non-real-time requirements -- Putting big data together -- Managing different data types -- Integrating data types into a big data environment -- 3. Old meets new: distributed computing -- Brief history of distributed computing -- Giving hanks to DARPA -- The value of a consistent model -- Understanding the basics of distributed computing -- Why we need distributed computing for big data -- The changing economics of computing -- The problem with latency -- Demand meets solutions -- Getting performance right. pt. II. technology foundations for big data -- 4. Digging into the big data technology components -- Exploring the big data stack -- Redundant physical infrastructure -- Physical redundant networks -- Managing hardware : storage and servers -- Infrastructure operations -- Security infrastructure -- Interfaces and feeds to and from applications and the internet -- Operational databases -- Organizing data services and tools -- Analytical data warehouses -- Big data analytics -- Big data applications -- 5. Virtualization and how it supports distributed computing -- Understanding the basics of virtualization -- The importance of virtualization to big data -- Server virtualization -- Application virtualization -- Network virtualization -- Processor and memory virtualization -- Data and storage virtualization -- Managing virtualization with the Hypervisor -- Abstraction and virtualization -- Implementing virtualization to work with big data -- 6. Examining the cloud and big data -- Defining the cloud in the context of big data -- Understanding cloud deployment and delivery models -- The cloud as an imperative for big data -- Making use of the cloud for big data -- Providers in the big data cloud market -- Amazon's public Elastic Compute Cloud -- Google big data services -- Microsoft Azure -- OpenStack -- Where to be careful when using cloud services. pt. III. Big data management -- 7. Operational databases -- RDBMs are important in a big data environment -- PostgreSQL relational database -- Nonrelational databases -- Key-value pair databases -- Riak key-value database -- Document databases -- MongoDB -- CouchDB -- Columnar databases -- HBase columnar database -- Graph databases --Neo4J graph database -- Spatial databases -- PostGIS/OpenGEO Suite -- Polyglot persistence -- 8. MapReduce fundamentals -- Tracing the origins of MapReduce -- Understanding the map function -- Adding the reduce function -- Putting map and reduce together -- Optimizing MapReduce tasks -- Hardware/network topology -- Synchronization -- File system -- 9. Exploring the world of Hadoop -- Explaining Hadoop -- Understanding the Hadoop Distributed File system (HDFS) -- NameNodes -- Data nodes -- Under the covers of HDFS -- Hadoop MapReduce -- 10. The Hadoop foundation and ecosystem -- Building a big data foundation with the Hadoop ecosystem -- Managing resources and applications with Hadoop YARN -- Storing big data with HBase -- Mining big data with Hive -- Interacting with the Hadoop ecosystem -- Pig and Pig Latin -- Sqoop -- Zookeeper -- 11. Appliances and big data warehouses -- Integrating big data with the traditional data warehouse -- Optimizing the data warehouse -- Differentiating big data structures from data warehouse data -- Examining a hybrid process case study -- Big data analysis and the data warehouse -- The integration lynchpin -- Rethinking extraction, transformation, and loading -- Changing the role of the data warehouse -- Changing deployment models in the Big data era -- The appliance model -- The cloud model -- Examining the future of data warehouses. pt. IV. Analytics and big data -- 12. Defining big data analytics -- Using big data to get results -- Basic analytics -- Advanced analytics -- Operationalized analytics -- Monetizing analytics -- Modifying business intelligence products to handle big data -- Data -- Analytical algorithms -- Infrastructure support -- Studying big data analytics examples -- Orbitz -- Nokia -- NASA -- Big data analytics solutions -- 13. Understanding text analytics and big data -- Exploring unstructured data -- Understanding text analytics -- Difference between text analytics and search -- Analysis and extraction techniques -- Understanding the extracted information -- Taxonomies -- Putting your results together with structured data -- Putting big data to use -- Voice of the customer -- Social media analytics -- Text analytics tools for big data -- Attensity -- Clarabridge -- IBM -- OpenText -- SAS -- 14. Customized approaches for analysis of big data. pt. V. Big data implementation -- 15. Integrating data sources -- Identifying the data you need -- Exploratory stage -- Codifying stage -- Integration and incorporation stage -- Understanding the fundamentals of big data integration -- Defining traditional ETL -- Data transformation -- Understanding ELT : extract, load, and transform -- Prioritizing big data quality -- Using Hadoop as ETL -- Best practices for data integration in a big data world -- 16. Dealing with real-time data streams and complex event processing -- Explaining streaming data and complex event processing -- Using streaming data -- Data streaming -- The need for metadata in streams -- Using complex event processing -- Differentiating CEP from streams -- Understanding the impact of streaming data and CEP on business -- 17. Operationalizing big data -- Making big data a part of your operational process -- Integrating big data -- Incorporating big data into the diagnosis of diseases -- Understanding big data workflows -- Workload in context to the business problem -- Ensuring the validity, veracity, and volatility of big data -- 18. Applying big data within your organization -- Figuring the economics of big data -- Identification of data types and sources -- Business process modifications or new process creation -- The technology impact of big data workflows -- Finding the talent to support big data projects -- Calculating the return on investment (ROI) from big data investments -- Enterprise data management and big data -- Defining enterprise data management -- Creating a big data implementation road map -- Understanding business urgency -- Projecting the right amount of capacity -- Selecting the right software development methodology -- Balancing budgets and skill sets -- Determining your appetite for risk -- Starting your big data road map -- 19. Security and governance for big data environments -- Security in context with big data -- Assessing the risk for the business -- Risks lurking inside big data -- Understanding data protection options -- The data governance challenge -- Auditing your big data process -- Identifying the key stakeholders -- Putting th right organizational structure in place -- Preparing for stewardship and management of risk -- Setting the right governance and quality policies -- Developing a well-governed and secure big data environment. pt. VI. Big data solutions in the real world -- 20. The importance of big data to business -- Big data as business planning tool -- Planning with data -- Doing the analysis -- Checking the results -- Acting on the plan -- Adding new dimensions to the planning cycle -- Monitoring in real time -- Adjusting the impact -- Enabling experimentation -- Keeping data analytics in perspective -- Getting started with the right foundation -- Getting your big data strategy started -- Planning for big data -- Transforming business processes with big data -- 21.

Analyzing data in motion : a real-world view -- Understanding companies' needs for data in motion -- The value of streaming data -- Streaming data with an environmental impact -- Using sensors to provide real-time information about rivers and oceans -- The benefits of real-time data -- Streaming data with a public policy impact -- Streaming data in the healthcare industry -- Capturing the data stream -- Streaming data in the energy industry -- Using streaming data to increase energy efficiency -- Using streaming data to advance the production of alternative sources of energy -- Connecting streaming data to historical and other real-time data sources -- 22. Improving business processes with big data analytics: a real-world view -- Understanding companies' needs for big data analytics -- Improving the customer experience with text analytics -- The business value to the big data analytics implementation -- Using big data analytics to determine next best action -- Preventing fraud with big data analytics -- The business benefit of integrating new sources of data. pt. VII. The part of tens -- 23. Ten big data best practices -- 24. Ten great big data resources -- Hurwitz & Associates -- Standards organizations -- The Open Data Foundation -- The Cloud Security Alliance -- National Institute of Standards and Technology -- apache Software Foundation -- OASIS -- Vendor sites -- Online collaborative sites -- Big data conferences -- 25. Ten big data do's and don'ts -- Glossary.

Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you\'ll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You\'ll learn what it is, why it matters, and how to choose and implement solutions that work.Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionalsAuthors are experts in information management, big data, and a variety of solutionsExplains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much moreProvides essential information in a no-nonsense, easy-to-understand style that is empoweringBig Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.