| Description |
1 online resource (378 pages) : illustrations |
| Note |
Includes index. |
| Summary |
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. |
| Contents |
Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Chapter 1: Introduction -- Objective -- Origin of Data Processing and Storage in the Computer Era -- Evolution of Databases and Codd Rules -- Transitioning to the World of Data Warehouses -- Data Warehouse Concepts -- Data Sources (Data Format and Common Sources) -- ETL (Extract, Transform, Load) -- ETL and ELT -- Data Mart -- Data Mart Architecture -- Advantages of Data Marts -- Examples of Data Marts -- Data Modeling -- Tabular Modeling -- Dimensional Modeling |
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Understanding Dimensional Modeling in Brief -- Dimensions -- Facts -- Measures -- Schematics Facts and Dimension Structuring -- Cubes and Reporting -- OLAP -- Online Analytical Processing, Cubes, Reporting, and Data Mining -- OLAP and Cubes -- Categorization of OLAP -- Querying Technique -- Reporting Techniques -- Data Mining -- Metadata -- Data Storage Techniques and Options -- Evolution of Big Data Technologies and Data Lakes -- Transition to the Modern Data Warehouse -- Traditional Big Data Technologies -- The Emergence of Data Lakes -- The Benefits of Data Lakes |
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Data Lakes as Data Warehouses -- Data Lake House and Data Mesh -- Transformation and Optimization between New vs. Old (Evolution to Data Lake House) -- A Wider Evolving Concept Called Data Mesh -- Building an Effective Data Engineering Team -- An Enterprise Scenario for Data Warehousing -- Summary -- Chapter 2: Modern Data Warehouses -- Objectives -- Introduction to Characteristics of Modern Data Warehouse -- Data Velocity -- Data Variety -- Volume -- Data Value -- Fault Tolerance -- Scalability -- Interoperability -- Reliability |
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Modern Data Warehouse Features: Distributed Processing, Storage, Streaming, and Processing Data in the Cloud -- Distributed Processing -- Flexibility and Speed in Implementation -- Flexibility and Speed in Processing -- Flexibility and Better Control on Costs -- Storage -- Storage as a Service -- Storage Solutions -- In-memory Storage -- Streaming and Processing -- Autonomous Administration Capabilities -- Self-driving -- Self-tuning and Configuration -- Multi-tenancy and Security -- Performance -- Storage Efficiency -- Scalable Storage -- Reliability, Availability, and Serviceability (RAS): |
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Multiple Parallel Processing (MPP) -- Flexibility and Speed in Implementation -- Real-time Processing -- Big Data -- CAP Theorem -- What Are NoSQL Databases? -- Key-Value Pair Stores -- Document Databases -- Columnar DBs -- Graph Databases -- Case Study: Enterprise Scenario for Modern Cloud-based Data Warehouse -- Advantages of Modern Data Warehouse over Traditional Data Warehouse -- Summary -- Chapter 3: Data Lake, Lake House, and Delta Lake -- Structure -- Objectives -- Data Lake, Lake House, and Delta Lake Concepts -- Data Lake, Storage, and Data Processing Engines Synergies and Dependencies |
| Subject |
Amazon Web Services (Firm)
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Data warehousing.
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Microsoft Azure (Computing platform)
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Big data.
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Business enterprises -- Data processing.
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Entrepôts de données (Informatique) |
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Microsoft Azure (Plateforme informatique) |
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Données volumineuses. |
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Entreprises -- Informatique. |
| Added Author |
Mishra, Abhishek, author.
|
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Kumar, S. (Sanjeev), author.
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| Other Form: |
Original 9798868800283 (OCoLC)1399463052 |
| ISBN |
9798868800290 (electronic bk.) |
| Standard No. |
10.1007/979-8-8688-0029-0 doi |
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