Architecting a Modern Data Warehouse for Large EnterprisesКНИГИ » ОС И БД
Название: Architecting a Modern Data Warehouse for Large Enterprises Автор: Anjani Kumar, Abhishek Mishra, Sanjeev Kumar Издательство: Apress Год: 2024 Страниц: 378 Язык: английский Формат: pdf Размер: 10.1 MB
Design and architect new generation cloud-based data warehouses using Azure and AWS. This book provides an in-depth understanding of how to build modern cloud-native data warehouses, as well as their history and evolution. The book starts by covering foundational data warehouse concepts, and introduces modern features such as distributed processing, big data storage, data streaming, and processing data on the cloud. You will gain an understanding of the synergy, relevance, and usage data warehousing standard practices in the modern world of distributed data processing. The authors walk you through the essential concepts of Data Mesh, Data Lake, Lakehouse, and Delta Lake. And they demonstrate the services and offerings available on Azure and AWS that deal with data orchestration, data democratization, data governance, data security, and business intelligence. After completing this book, you will be ready to design and architect enterprise-grade, cloud-based modern data warehouses using industry best practices and guidelines.
What You Will Learn:
Understand the core concepts underlying modern data warehouses Design and build cloud-native data warehousesGain a practical approach to architecting and building data warehouses on Azure and AWS Implement modern data warehousing components such as Data Mesh, Data Lake, Delta Lake, and Lakehouse Process data through pandas and evaluate your model’s performance using metrics such as F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications
Who This Book Is For: Experienced developers, cloud architects, and technology enthusiasts looking to build cloud-based modern data warehouses using Azure and AWS.
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