This text should be required reading for everyone in contemporary business. Peter woodhull ceo modus21 the one book that clearly describes and links big data concepts to business utility. Dr. Christopher starr phd simply this is the best big data book on the market. Sam rostam cascadian it group. One of the most contemporary approaches I ve seen to big data fundamentals. Joshua m. Davis phd the definitive plain english guide to big data for business and technology professionals big data fundamentals provides a pragmatic no nonsense introduction to big data. Best selling it author thomas erl and his team clearly explain key big data concepts theory and terminology as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how big data can propel an organization forward by solving a spectrum of previously intractable business problems. Next they demystify key analysis techniques and technologies and show how a big data solution environment can be built and integrated to offer competitive advantages. Discovering big data s fundamental concepts and what makes it different from previous forms of data analysis and data scienceunderstanding the business motivations and drivers behind big data adoption from operational improvements through innovationplanning strategic business driven big data initiativesaddressing considerations such as data management governance and securityrecognizing the 5 v characteristics of datasets in big data environments volume velocity variety veracity and valueclarifying big data s relationships with oltp olap etl data warehouses and data martsworking with big data in structured unstructured semi structured and metadata formatsincreasing value by integrating big data resources with corporate performance monitoringunderstanding how big data leverages distributed and parallel processingusing nosql and other technologies to meet big data s distinct data processing requirementsleveraging statistical approaches of quantitative and qualitative analysisapplying computational analysis methods including machine learning