Transform enterprise big data into valuable assets with this comprehensive guide to data analysis, data engineering, algorithm design and data architecture.
Transform enterprise big data into valuable assets with this comprehensive guide to data analysis, data engineering, algorithm design and data architecture.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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