Less than 0.5 per cent of all data is currently analysed and used. However, business leaders and managers cannot afford to be unconcerned or sceptical about data. Data is revolutionizing the way we work and it is the companies that view data as a strategic asset that will survive and thrive. Bernard Marr's Data Strategy is a must-have guide to creating a robust data strategy. Explaining how to identify your strategic data needs, what methods to use to collect the data and, most importantly, how to translate your data into organizational insights for improved business decision-making and performance, this is essential reading for anyone aiming to leverage the value of their business data and gain competitive advantage.
Packed with case studies and real-world examples, advice on how to build data competencies in an organization and crucial coverage of how to ensure your data doesn't become a liability, Data Strategy will equip any organization with the tools and strategies it needs to profit from big data, analytics and the Internet of Things.
Big Data: A Business and Legal Guide supplies a clear understanding of the interrelationships between Big Data, the new business insights it reveals, and the laws, regulations, and contracting practices that impact the use of the insights and the data. Providing business executives and lawyers (in-house and in private practice) with an accessible primer on Big Data and its business implications, this book will enable readers to quickly grasp the key issues and effectively implement the right solutions to collecting, licensing, handling, and using Big Data.The book brings togetherÂ subject matter experts whoÂ examine a different area of law in each chapter and explain how these laws can affect the way your business or organization can use Big Data. These experts also supply recommendations as to the steps your organization can take to maximize Big Data opportunities without increasing risk and liability to your organization.
Taking a cross-disciplinary approach, the book will help executives, managers, and counsel better understand the interrelationships between Big Data, decisions based on Big Data, and the laws, regulations, and contracting practices that impact its use. After reading this book, you will be able to think more broadly about the best way to harness Big Data in your business and establish procedures to ensure that legal considerations are part of the decision.
There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data resources, including big data, to improve decision making.Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining.
Filled with examples that illustrate the value of analytics throughout, the book outlines a conceptual framework for data modeling that can help you immediately improve your own analytics and decision-making processes. It also provides in-depth coverage of analyzing unstructured data with text mining methods to supply you with the well-rounded understanding required to leverage your information assets into improved strategic decision making.
Spark: Big Data Cluster Computing in Production goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production. Written by an expert team well-known in the big data community, this book walks you through the challenges in moving from proof-of-concept or demo Spark applications to live Spark in production. Real use cases provide deep insight into common problems, limitations, challenges, and opportunities, while expert tips and tricks help you get the most out of Spark performance. Coverage includes Spark SQL, Tachyon, Kerberos, ML Lib, YARN, and Mesos, with clear, actionable guidance on resource scheduling, db connectors, streaming, security, and much more.
Spark has become the tool of choice for many Big Data problems, with more active contributors than any other Apache Software project. General introductory books abound, but this book is the first to provide deep insight and real-world advice on using Spark in production. Specific guidance, expert tips, and invaluable foresight make this guide an incredibly useful resource for real production settings.
Spark works with other big data tools including MapReduce and Hadoop, and uses languages you already know like Java, Scala, Python, and R. Lightning speed makes Spark too good to pass up, but understanding limitations and challenges in advance goes a long way toward easing actual production implementation. Spark: Big Data Cluster Computing in Production tells you everything you need to know, with real-world production insight and expert guidance, tips, and tricks.
Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing.
The book begins with a brief introduction to the Big Data world and its current industry standards, with an introduction to the R language presenting its development, structure, applications in the real world, and its shortcomings. The book then progresses towards the revision of major R functions for data management and transformations. You'll be introduced to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters), and be given guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. In addition to this, Big Data Analytics with R expands to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks, including other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.
Simon Walkowiak is a cognitive neuroscientist and a managing director of Mind Project Ltd - a Big Data and Predictive Analytics consultancy based in London, United Kingdom. As a former data curator at the UK Data Service (UKDS, University of Essex) European largest socio-economic data repository, Simon has an extensive experience in processing and managing large-scale datasets such as censuses, sensor and smart meter data, telecommunication data and well-known governmental and social surveys such as the British Social Attitudes survey, Labour Force surveys, Understanding Society, National Travel survey, and many other socio-economic datasets collected and deposited by Eurostat, World Bank, Office for National Statistics, Department of Transport, NatCen and International Energy Agency, to mention just a few. Simon has delivered numerous data science and R training courses at public institutions and international companies. He has also taught a course in Big Data Methods in R at major UK universities and at the prestigious Big Data and Analytics Summer School organized by the Institute of Analytics and Data Science (IADS).
Hadoop has changed the way large data sets are analyzed, stored, transferred, and processed.Â At such low cost, itÂ provides benefits like supports partial failure, fault tolerance, consistency, scalability, flexible schema,Â and so on. It also supports cloud computing. More and more number of individuals are looking forward to mastering their Hadoop skills.
While initiating with Hadoop, most users are unsure about how to proceed with Hadoop.Â They are not aware of what are the pre-requisite or data structure they should be familiar with. OrÂ How to make the most efficient use ofÂ Hadoop and its ecosystem. To help them with all these queries and other issues this e-book is designed.
The book gives insights into many of Hadoop libraries and packages that are not knownÂ to many Big data Analysts and Architects. The e-book also tells you about Hadoop MapReduce and HDFS. The example in the e-book is well chosen and demonstrates how to control Hadoop ecosystem through various shell commands.Â With this book,Â users will gain expertise in Hadoop technology and its related components.Â The book leverages you with the best Hadoop content with the lowest price range.
After going through this book, you will also acquire knowledge on Hadoop Security required for Hadoop Certifications like CCAH and CCDH. It is a definite guide to Hadoop.
Chapter 1: What Is Big Data
Chapter 2: Introduction to Hadoop
Chapter 3: Hadoop Installation
Chapter 4: HDFS
Chapter 5: Mapreduce
Chapter 6: First Program
Chapter 7: Counters & Joins In MapReduce
Chapter 8: MapReduce Hadoop Program To Join Data
Chapter 9: Flume and Sqoop
Chapter 10: Pig
Chapter 11: OOZIE
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