With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge.Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape.Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system.Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark’s scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance.Key FeaturesThis book describes the Big Data ecosystemIt includes real-world instances of big data issuesExplain how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reportingHow we can get value out of Big Data by following a structural process?Recognize the differences between a standard database management system and a big data management systemHow to choose a data model that fits your data’s properties?How to get data out of a database or a large data management system?Recognize when a large data problem necessitates data integrationExplore and prepare data for modelling using machine learning techniquesAnalysis of big data problems with Spark’s scalable machine learning techniquesModeling a big data problem into a graph database and performing scalable analytical operations over the graphDiscuss different tools and techniques for processing big dataBig data applications in healthcare and finance

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