Описание: Improve your analytics and data platform to solve major challenges, including operationalizing big data and advanced analytics workloads on Azure. You will learn how to monitor complex pipelines, set alerts, and extend your organization's custom monitoring requirements.
This book starts with an overview of the Azure Data Factory as a hybrid ETL/ELT orchestration service on Azure. The book then dives into data movement and the connectivity capability of Azure Data Factory. You will learn about the support for hybrid data integration from disparate sources such as on-premise, cloud, or from SaaS applications. Detailed guidance is provided on how to transform data and on control flow. Demonstration of operationalizing the pipelines and ETL with SSIS is included. You will know how to leverage Azure Data Factory to run existing SSIS packages. As you advance through the book, you will wrap up by learning how to create a single pane for end-to-end monitoring, which is a key skill in building advanced analytics and big data pipelines.
What You'll Learn
Understand data integration on Azure cloudBuild and operationalize an ADF pipelineModernize a data warehouseBe aware of performance and security considerations while moving data
Who This Book Is For
Data engineers and big data developers. ETL (extract, transform, load) developers also will find the book useful in demonstrating various operations.
Автор: Vincent Lemaire; Simon Malinowski; Anthony Bagnall Название: Advanced Analytics and Learning on Temporal Data ISBN: 3030390977 ISBN-13(EAN): 9783030390976 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Wurzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions.
Описание: Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. This book is your guide to getting ...
Автор: Bouarara Hadj Ahmed Название: Advanced Deep Learning Applications in Big Data Analytics ISBN: 1799827917 ISBN-13(EAN): 9781799827917 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 30723.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today's digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.
Автор: Giabbanelli Philippe J., Mago Vijay K., Papageorgiou Elpiniki I. Название: Advanced Data Analytics in Health ISBN: 3030085716 ISBN-13(EAN): 9783030085711 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning.
Автор: Giabbanelli Название: Advanced Data Analytics in Health ISBN: 3319779109 ISBN-13(EAN): 9783319779102 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.
Автор: Bouarara Hadj Ahmed Название: Advanced Deep Learning Applications in Big Data Analytics ISBN: 1799827925 ISBN-13(EAN): 9781799827924 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 23199.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Explores architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is designed for engineers, data analysts, data scientists, IT specialists, marketers, researchers, academics, and students.
Описание: This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions.
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