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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.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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