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E-grāmata: Enterprise Big Data Framework: Building Critical Capabilities to Win in the Data Economy

4.33/5 (6 ratings by Goodreads)
  • Formāts: 496 pages
  • Izdošanas datums: 03-Nov-2023
  • Izdevniecība: Kogan Page Ltd
  • Valoda: eng
  • ISBN-13: 9781398601727
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Enterprise Big Data Framework: Building Critical Capabilities to Win in the Data EconomyPalielināt
  • Formāts: 496 pages
  • Izdošanas datums: 03-Nov-2023
  • Izdevniecība: Kogan Page Ltd
  • Valoda: eng
  • ISBN-13: 9781398601727
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Transform enterprise big data into valuable assets with this comprehensive guide to data analysis, data engineering, algorithm design and data architecture.

Businesses who can make sense of the huge influx and complexity of data will be the big winners in the information economy. Learning how to fully leverage, analyze and integrate big data can help companies reduce costs, increase operating margins and add income.

This comprehensive guide covers all the aspects of transforming enterprise data into value, from the initial set-up of a big data strategy, towards algorithms, architecture and data governance processes. Using a vendor-independent approach, The Enterprise Big Data Framework offers practical advice on how to develop data-driven decision making, detailed data analysis and data engineering techniques.

With a focus on business implementation, The Enterprise Big Data Framework includes sections on analysis, engineering, algorithm design and big data architecture, and covers topics such as data preparation and presentation, data modelling, data science, programming languages and machine learning algorithms. Endorsed by leading accreditation and examination institute AMPG International, this book is required reading for the Enterprise Big Data Certifications, which aim to develop excellence in big data practices across the globe.

Recenzijas

"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited * "If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate * "This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *

Section - ONE: Introduction to Big Data;


Chapter - 01: Introduction to Big Data;
Chapter - 02: The Big Data framework;
Chapter - 03: Big Data strategy;
Chapter - 04: Big Data architecture;
Chapter - 05: Big Data algorithms;
Chapter - 06: Big Data processes;
Chapter - 07: Big Data functions;
Chapter - 08: Artificial intelligence;


Section - TWO: Enterprise Big Data analysis;


Chapter - 09: Introduction to Big Data analysis;
Chapter - 10: Defining the business objective;
Chapter - 11: Data ingestion importing and reading data sets;
Chapter - 12: Data preparation cleaning and wrangling data;
Chapter - 13: Data analysis model building;
Chapter - 14: Data presentation;


Section - THREE: Enterprise Big Data engineering;


Chapter - 15: Introduction to Big Data engineering;
Chapter - 16: Data modelling;
Chapter - 17: Constructing the data lake;
Chapter - 18: Building an enterprise Big Data warehouse;
Chapter - 19: Design and structure of Big Data pipelines;
Chapter - 20: Managing data pipelines;
Chapter - 21: Cluster technology;


Section - FOUR: enterprise Big Data algorithm design;


Chapter - 22: Introduction to Big Data algorithm design;
Chapter - 23: Algorithm design fundamental concepts;
Chapter - 24: Statistical machine learning algorithms;
Chapter - 25: The data science roadmap;
Chapter - 26: Programming languages 26 visualization and simple metrics;
Chapter - 27: Advanced machine learning algorithms;
Chapter - 28: Advanced machine learning classification algorithms;
Chapter - 29: Technical communication and documentation;


Section - FIVE: Enterprise Big Data architecture;


Chapter - 30: Introduction to the Big Data architecture;
Chapter - 31: Strength and resilience the Big Data platform;
Chapter - 32: Design principles for Big Data architecture;
Chapter - 33: Big Data infrastructure;
Chapter - 34: Big Data platforms;
Chapter - 35: The Big Data application provider;
Chapter - 36: System orchestration in Big Data
Jan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.
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