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Cloud scale analytics with Azure data services : build modern data warehouses on Microsoft Azure /
A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key Features Store and analyze data with enterprise-grade security and auditing Perform batch, streaming, and interactive analytics to optimize your big data solutions with ease Develop and run parallel dat...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2021.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Section 1: Data Warehousing and Considerations Regarding Cloud Computing
- Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses
- Distinguishing between Data Warehouses and Data Lakes
- Understanding Data Warehouse patterns
- Investigating ETL/ELT
- Understanding Data Warehouse layers
- Implementing reporting and dashboarding
- Loading bigger amounts of data
- Starting with Data Lakes
- Understanding the Data Lake ecosystem
- Comparing Data Lake zones
- Discovering caveats
- Understanding the opportunities of modern cloud computing
- Understanding Infrastructure-as-a-Service
- Understanding Platform-as-a-Service
- Understanding Software-as-a-Service
- Examining the possibilities of virtual machines
- Understanding Serverless Functions
- Looking at the importance of containers
- Exploring the advantages of scalable environments
- Implementing elastic storage and compute
- Exploring the benefits of AI and ML
- Understanding ML challenges
- Sorting ML into the Modern Data Warehouse
- Understanding responsible ML/AI
- Answering the question
- Summary
- Chapter 2: Connecting Requirements and Technology
- Formulating your requirements
- Asking in the right direction
- Understanding basic architecture patterns
- Examining the scalable storage component
- Looking at data integration
- Sorting in compute
- Adding a presentation layer
- Planning for dashboard/reporting
- Adding APIs/API management
- Relying on SSO/MFA/networking
- Not forgetting DevOps and CI/CD
- Finding the right Azure tool for the right purpose
- Understanding Industry Data Models
- Thinking about different sizes
- Planning for S size
- Planning for M size
- Planning for L size
- Understanding the supporting services
- Requiring data governance
- Establishing security
- Establishing DevOps and CI/CD
- Summary
- Questions
- Section 2: The Storage Layer
- Chapter 3: Understanding the Data Lake Storage Layer
- Technical requirements
- Setting up your Cloud Big Data Storage
- Provisioning a standard storage account instead
- Creating an Azure Data Lake Gen2 storage account
- Organizing your data lake
- Talking about zones in your data lake
- Creating structures in your data lake
- Planning the leaf level
- Understanding data life cycles
- Investigating storage tiers
- Planning for criticality
- Setting up confidentiality
- Using filetypes
- Implementing a data model in your Data Lake
- Understanding interconnectivity between your data lake and the presentation layer
- Examining key implementation and usage
- Monitoring your storage account
- Creating alerts for Azure storage accounts
- Talking about backups
- Configuring delete locks for the storage service
- Backing up your data
- Implementing access control in your Data Lake
- Understanding RBAC