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The Informed Company How to Build Modern Agile Data Stacks That Drive Winning Insights.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Newark :
John Wiley & Sons, Incorporated,
2021.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright Page
- Contents
- About This Book
- Why Write This Book
- Who This Book Is For
- Who This Book Is Not For
- Who Wrote the Book
- Who Edited the Book
- Influences
- How This Book Was Written
- How to Read This Book
- Foreword
- Introduction
- Merging Business Context with Data Information
- The Four Stages of Agile Data Organization
- Stage 1 Source aka Siloed Data
- Chapter 1 Starting with Source Data
- Common Options for Analyzing Source Data
- Chapter 2 The Need to Replicate Source Data
- Replicate Sources
- Create Read-OnlyAccess
- Chapter 3 Source Data Best Practices
- Keep a Complexity Wiki Page
- Snippet Dictionary
- Use a BI Product
- Double Check Results
- Keep Short Dashboards
- Design Before Building
- Stage 2 Data Lake aka Data Combined
- Chapter 4 Why Build a Data Lake?
- What Is a Data Lake?
- Reasons to Build a Data Lake Summarized
- Chapter 5 Choosing an Engine for the Data Lake
- Modern Columnar Warehouse Engines
- Modern Warehouse Engine Products
- Database Engines
- Recommendation
- Chapter 6 Extract and Load (EL) Data
- ETL versus ELT
- EL/ETL Vendors
- Extract Options
- Load Options
- Multiple Schemas
- Other Extract and Load Routes
- Chapter 7 Data Lake Security
- Access in Central Place
- Permission Tiers
- Chapter 8 Data Lake Maintenance
- Why SQL?
- Data Sources
- Performance
- Upgrade Snippets to Views
- Stage 3 Data Warehouse aka the Single Source of Truth
- Chapter 9 The Power of Layers and Views
- Make Readable Views
- Layer Views on Views
- Start with a Single View
- Chapter 10 Staging Schemas
- Orient to the Schemas
- Pick a Table and Clean It
- Other Staging Modeling Considerations
- Building on Top of Staging Schemas
- Chapter 11 Model Data with dbt
- Version Control
- Modularity and Reusability
- Package Management.
- Organizing Files
- Macros
- Incremental Tables
- Testing
- Chapter 12 Deploy Modeling Code
- Branch Using Version Control Software
- Commit Message
- Test Locally
- Code Review
- Schedule Runs
- Chapter 13 Implementing the Data Warehouse
- Manage Dependencies
- Combine Tables Within Schemas
- Combine Tables Across Schemas
- Keep the Grain Consistent
- Create Business Metrics
- Keeping Accurate History
- Chapter 14 Managing Data Access
- How to Secure Sensitive Data in the Data Warehouse
- How to Secure Sensitive Data in a BI Tool
- Chapter 15 Maintaining the Source of Truth
- Track New Metrics
- Deprecate Old Metrics
- Deprecate Old Schemas
- Resolve Conflicting Numbers
- Handling Ongoing Requests and Ongoing Feedback
- Updating Modeling Code
- Manage Access
- Tuning to Optimize
- Code Review All Modeling
- Maintenance Checklist
- Stage 4 Data Marts aka Data Democratized
- Chapter 16 Data Mart Implementation
- Views on the Data Warehouse
- Segment Tables
- Access Update
- Chapter 17 Data Mart Maintenance
- Educate Team
- Identifies Issues
- Identify New Needs
- Help Track Success
- Chapter 18 Modern versus Traditional Data Stacks: What's Changed?
- What's Changed?
- Chapter 19 Row- versus Column-Oriented Database
- Row-Oriented Databases
- Column-Oriented Databases
- Summary
- Chapter 20 Style Guide Example
- Simplify
- Clean
- Naming Conventions
- Share It
- Chapter 21 Building an SST Example
- First Attempt-Same Tables with Prefixes
- Second Attempt-Operational Schema (Source Agnostic)
- Third Attempt-Application Separate, Other Sources Smashed
- Less Planning, More Implementing
- Acknowledgments and Contributions
- Thank-yous
- Index
- EULA.