Andreas Francois Vermeulen – författare
Visar alla böcker från författaren Andreas Francois Vermeulen. Handla med fri frakt och snabb leverans.
8 produkter
8 produkter
E-bok
PDF, Engelska, 2016896 kr
Läs direkt efter köp
Dive into the world of SQL on Hadoop and get the most out of your Hive data warehouses. This book is your go-to resource for using Hive: authors Scott Shaw, Ankur Gupta, David Kjerrumgaard, and Andreas Francois Vermeulen take you through learning HiveQL, the SQL-like language specific to Hive, to analyze, export, and massage the data stored across your Hadoop environment. From deploying Hive on your hardware or virtual machine and setting up its initial configuration to learning how Hive interacts with Hadoop, MapReduce, Tez and other big data technologies, Practical Hive gives you a detailed treatment of the software.
In addition, this book discusses the value of open source software, Hive performance tuning, and how to leverage semi-structured and unstructured data.
What You Will Learn
Install and configure Hive for new and existing datasetsPerform DDL operationsExecute efficient DML operationsUse tables, partitions, buckets, and user-defined functionsDiscover performance tuning tips and Hive best practicesWho This Book Is For
Developers, companies, and professionals who deal with large amounts of data and could use software that can efficiently manage large volumes of input. It is assumed that readers have the ability to work with SQL.
Häftad, Engelska, 2016
672 kr
Skickas inom 10-15 vardagar
Dive into the world of SQL on Hadoop and get the most out of your Hive data warehouses. This book is your go-to resource for using Hive: authors Scott Shaw, Ankur Gupta, David Kjerrumgaard, and Andreas Francois Vermeulen take you through learning HiveQL, the SQL-like language specific to Hive, to analyze, export, and massage the data stored across your Hadoop environment. From deploying Hive on your hardware or virtual machine and setting up its initial configuration to learning how Hive interacts with Hadoop, MapReduce, Tez and other big data technologies, Practical Hive gives you a detailed treatment of the software.In addition, this book discusses the value of open source software, Hive performance tuning, and how to leverage semi-structured and unstructured data. What You Will LearnInstall and configure Hive for new and existing datasetsPerform DDL operationsExecute efficient DML operationsUse tables, partitions, buckets, and user-defined functionsDiscover performance tuning tips and Hive best practicesWho This Book Is ForDevelopers, companies, and professionals who deal with large amounts of data and could use software that can efficiently manage large volumes of input. It is assumed that readers have the ability to work with SQL.
Häftad, Engelska, 2018
115 kr
Skickas
Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions.What You'll LearnBecome fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteriaMaster the methods for retrieving actionable business knowledgeCoordinate the handling ofpolyglot data types in a data lake for repeatable resultsWho This Book Is ForData scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers
E-bok
Engelska, 2018896 kr
Läs direkt efter köp
Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions.What You''ll LearnBecome fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteriaMaster the methods for retrieving actionable business knowledgeCoordinate the handling ofpolyglot data types in a data lake for repeatable resultsWho This Book Is ForData scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers
Häftad, Engelska, 2019
769 kr
Skickas inom 10-15 vardagar
Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory,supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors.What You Will LearnGenerate and identify transformational disruptors of artificial intelligence (AI)Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environmentHone the skills required to handle the future of data engineering and data scienceWho This Book Is ForIntermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management
E-bok
Engelska, 20191 059 kr
Läs direkt efter köp
Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science.
Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes.
Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory,supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors.
What You Will Learn
Generate and identify transformational disruptors of artificial intelligence (AI)Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environmentHone the skills required to handle the future of data engineering and data scienceWho This Book Is For
Intermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management
Häftad, Engelska, 2026
619 kr
Skickas inom 3-6 vardagar
Agentic Hyper-Personalized Dimensions – Six Dimensions of Business Dark Data explores how generative AI (Gen-AI), combined with agentic intelligence, can transform raw enterprise data into hyper-personalized, actionable insights. This book introduces a practical framework for building AI-powered agent swarms that operate across six critical dimensions of business dark data, empowering intelligent automation, decision augmentation, and strategic foresight at unprecedented scale.In an age when data is underutilized, organizations are overwhelmed by complexity, latency, and fragmentation. Traditional analytics pipelines fall short in handling dynamic environments and human-centric demands. This book addresses this critical gap by detailing how autonomous Gen-AI agents — built on foundational disciplines of data engineering, data science, and machine learning — can process, personalize, and operationalize hidden insights buried deep within enterprise systems. The six dimensions presented in the book form a cognitive and computational blueprint that guides the design, deployment, and evolution of agentic swarms. Each dimension focuses on a specific mode of intelligence — such as contextual reasoning, emotional alignment, or real-time adaptive sensing — and shows how AI agents can specialize within each domain to maximize business impact.Readers won’t just learn theory — they’ll explore field-tested methodologies to deploy Gen-AI-powered solutions at enterprise scale. From zoned data lakes and transformer models to swarm governance and consensus mechanisms, the book walks readers through the full life cycle of building intelligent systems that learn, evolve, and act with purpose. What makes this book essential — and unlike others in the market — is that the author is not writing from the sidelines. He is actively architecting and delivering these systems today, with firsthand insight into what works, what fails, and what the future holds. The methodology presented blends system design, human-aligned AI, industrial automation, and trusted decision-making, offering readers a rich and practical road map for AI-first enterprise innovation.What You Will LearnHow to structure AI-powered agents across six interlinked cognitive and computational dimensions to effectively process dark data and deliver hyper-personalized, real-time business insights.Practical knowledge on how to orchestrate swarms of Gen-AI agents — governed by Councils — that collaboratively manage complexity, detect emerging patterns, and operate autonomously in enterprise environments.The foundational and advanced skills required to implement generative AI and agent-based intelligence within large-scale, multi-cloud, and hybrid business ecosystems.Who This Book is ForThis book is designed for professionals and practitioners operating at the intersection of artificial intelligence and business transformation. It is ideal for AI developers, data scientists, solution architects, enterprise engineers, and technical managers who are looking to move beyond the hype of generative AI and apply it meaningfully within industrial and enterprise-grade ecosystems.
E-bok
Engelska, 2026896 kr
Läs direkt efter köp
Agentic Hyper-Personalized Dimensions – Six Dimensions of Business Dark Data explores how generative AI (Gen-AI), combined with agentic intelligence, can transform raw enterprise data into hyper-personalized, actionable insights. This book introduces a practical framework for building AI-powered agent swarms that operate across six critical dimensions of business dark data, empowering intelligent automation, decision augmentation, and strategic foresight at unprecedented scale.In an age when data is underutilized, organizations are overwhelmed by complexity, latency, and fragmentation. Traditional analytics pipelines fall short in handling dynamic environments and human-centric demands. This book addresses this critical gap by detailing how autonomous Gen-AI agents — built on foundational disciplines of data engineering, data science, and machine learning — can process, personalize, and operationalize hidden insights buried deep within enterprise systems. The six dimensions presented in the book form a cognitive and computational blueprint that guides the design, deployment, and evolution of agentic swarms. Each dimension focuses on a specific mode of intelligence — such as contextual reasoning, emotional alignment, or real-time adaptive sensing — and shows how AI agents can specialize within each domain to maximize business impact.Readers won’t just learn theory — they’ll explore field-tested methodologies to deploy Gen-AI-powered solutions at enterprise scale. From zoned data lakes and transformer models to swarm governance and consensus mechanisms, the book walks readers through the full life cycle of building intelligent systems that learn, evolve, and act with purpose. What makes this book essential — and unlike others in the market — is that the author is not writing from the sidelines. He is actively architecting and delivering these systems today, with firsthand insight into what works, what fails, and what the future holds. The methodology presented blends system design, human-aligned AI, industrial automation, and trusted decision-making, offering readers a rich and practical road map for AI-first enterprise innovation.What You Will LearnHow to structure AI-powered agents across six interlinked cognitive and computational dimensions to effectively process dark data and deliver hyper-personalized, real-time business insights.Practical knowledge on how to orchestrate swarms of Gen-AI agents — governed by Councils — that collaboratively manage complexity, detect emerging patterns, and operate autonomously in enterprise environments.The foundational and advanced skills required to implement generative AI and agent-based intelligence within large-scale, multi-cloud, and hybrid business ecosystems. Who This Book is ForThis book is designed for professionals and practitioners operating at the intersection of artificial intelligence and business transformation. It is ideal for AI developers, data scientists, solution architects, enterprise engineers, and technical managers who are looking to move beyond the hype of generative AI and apply it meaningfully within industrial and enterprise-grade ecosystems.