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The modern data warehouse in Azure : building with speed and agility on Microsoft's cloud platform /
Build a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast--fast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style pr...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress L.P.,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a How, Matt, |e author. | |
| 245 | 1 | 4 | |a The modern data warehouse in Azure : |b building with speed and agility on Microsoft's cloud platform / |c Matt How. |
| 264 | 1 | |a Berkeley, CA : |b Apress L.P., |c 2020. | |
| 300 | |a 1 online resource (297 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: The Rise of the Modern Data Warehouse -- Getting Started -- Multi-region Support -- Resource Groups and Tagging -- Azure Security -- Tools of the Trade -- Glossary of Terms -- Naming Conventions -- Chapter 2: The SQL Engine -- The Four Vs -- Azure Synapse Analytics -- Understanding Distributions -- The First Problem -- ROUND ROBIN Distribution -- HASH Distribution -- The Distribution Column -- How to Check if You Have the Right Column -- REPLICATED Distribution | |
| 505 | 8 | |a Resource Management -- Resource Classes -- Static Resource Classes -- Dynamic Resource Classes -- Pausing and Resuming the Warehouse -- Workload Management -- PolyBase -- Azure SQL Database -- The Cloud-Based OLTP Engine -- The Benefits of Azure SQL Database -- Improved Concurrency -- Trickle-Fed Data Warehouses -- Managing Slowly Changing Dimensions -- Intelligent Query Processing and Tuning -- Automatic Tuning -- Adaptive Query Processing -- Batch Mode Memory Grant Feedback -- Adaptive Joins -- Interleaved Execution -- Hyperscale -- The Hyperscale Architecture -- Accelerated Disaster Recovery | |
| 505 | 8 | |a Azure SQL Deployment Options -- Azure SQL Database Managed Instances -- Azure SQL Database Elastic Pools -- Azure SQL Database V-Core Tiers -- Azure Synapse Analytics vs. Azure SQL Database -- The Right Type of Data -- The Size of the Data -- The Frequency of the Data -- The Availability of the Data -- The Integration of Data -- Chapter 3: The Integration Engine -- Introduction to Azure Data Factory -- The Data Factory Building Blocks -- Linked Services -- Integration Runtimes -- Self-Hosted Integration Runtime -- Azure SSIS Integration Runtime -- Triggers -- Datasets -- Pipelines and Activities | |
| 505 | 8 | |a Activity Types -- External Compute Activities -- Internal Activities -- Iteration and Conditional Activities -- Web Activities -- Output Constraints -- Implementing Azure Data Factory -- Security in Azure Data Factory -- Using the Managed Service Identity -- Source Control of Azure Data Factory -- Templates -- Solution Structure -- Getting Started with Azure Data Factory -- Create Linked Services -- Creating Datasets -- Creating Pipelines -- Debugging Your Pipelines -- Monitoring Your Pipelines -- Parameter-Driven Pipelines -- Getting Started with Parameters -- Using the Lookup Activity | |
| 505 | 8 | |a Getting Started with the Lookup Activity -- Additional Azure Data Factory Elements -- Additional Invocation Methods -- Mapping Data Flows -- Multiple Inputs and Outputs -- Schema Modifier -- Row Modifier -- Execute Mapping Data Flows -- Azure Data Factory Processing Patterns -- Linear Pipelines -- Parent-Child Processing -- Iterative Parent-Child Processing -- Dynamic Column Mappings -- Partitioning Datasets -- Chapter 4: The Ingestion Architecture -- Layers of Curation -- The Raw Layer -- The Clean Layer -- The Transformed Layer -- Understanding Ingestion Architecture -- Batch Ingestion | |
| 500 | |a The Risks and Opportunities of Batch Ingestion | ||
| 520 | |a Build a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast--fast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Azure Analysis Services to empower data-driven decision making that drives your business forward toward a pattern of success. This book teaches you how to employ the Azure platform in a strategy to dramatically improve implementation speed and flexibility of data warehousing systems. You will know how to make correct decisions in design, architecture, and infrastructure such as choosing which type of SQL engine (from at least three options) best meets the needs of your organization. You also will learn about ETL/ELT structure and the vast number of accelerators and patterns that can be used to aid implementation and ensure resilience. Data warehouse developers and architects will find this book a tremendous resource for moving their skills into the future through cloud-based implementations. You will: Choose the appropriate Azure SQL engine for implementing a given data warehouse Develop smart, reusable ETL/ELT processes that are resilient and easily maintained Automate mundane developm ent tasks through tools such as PowerShell Ensure consistency of data by creating and enforcing data contracts Explore streaming and event-driven architectures for data ingestion Create advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouse. | ||
| 500 | |a Includes index. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Data warehousing. | |
| 650 | 0 | |a Microsoft Azure (Computing platform) | |
| 650 | 6 | |a Entrepôts de données (Informatique) | |
| 650 | 6 | |a Microsoft Azure (Plateforme informatique) | |
| 650 | 7 | |a Databases. |2 bicssc | |
| 650 | 7 | |a Microsoft programming. |2 bicssc | |
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| 650 | 7 | |a Computers |x Programming |x Microsoft Programming. |2 bisacsh | |
| 650 | 7 | |a Data warehousing |2 fast | |
| 650 | 7 | |a Microsoft Azure (Computing platform) |2 fast | |
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