فهرست مطالب

• Data Engineering with dbt
• Contributors
• About the author
• About the reviewers
• Preface
• Part 1: The Foundations of Data Engineering
• Chapter 1: The Basics of SQL to Transform Data
• Technical requirements
• Introducing SQL
• SQL basics – core concepts and commands
• Setting up a Snowflake database with users and roles
• Querying data in SQL – syntax and operators
• Combining data in SQL – the JOIN clause
• Advanced – introducing window functions
• Summary
• Further reading
• Chapter 2: Setting Up Your dbt Cloud Development Environment
• Technical requirements
• Setting up your GitHub account
• Setting up your dbt Cloud account
• Comparing dbt Core and dbt Cloud workflows
• Experimenting with SQL in dbt Cloud
• Introducing the source and ref dbt functions
• Summary
• Further reading
• Chapter 3: Data Modeling for Data Engineering
• Technical requirements
• What is and why do we need data modeling?
• Conceptual, logical, and physical data models
• Entity-Relationship modeling
• Modeling use cases and patterns
• Common problems in data models
• Modeling styles and architectures
• Summary
• Further reading
• Chapter 4: Analytics Engineering as the New Core of Data Engineering
• Technical requirements
• The data life cycle and its evolution
• Understanding the modern data stack
• Defining analytics engineering
• DataOps – software engineering best practices for data
• Summary
• Further reading
• Chapter 5: Transforming Data with dbt
• Technical requirements
• The dbt Core workflow for ingesting and transforming data
• Introducing our stock tracking project
• Defining data sources and providing reference data
• How to write and test transformations
• Summary
• Part 2: Agile Data Engineering with dbt
• Chapter 6: Writing Maintainable Code
• Technical requirements
• Writing code for humans
• Creating the architectural layers
• Saving history is crucial
• Summary
• Chapter 7: Working with Dimensional Data
• Adding dimensional data
• Loading the data of the first dimension
• Building the STG model for the first dimension
• Saving history for the dimensional data
• Building the REF layer with the dimensional data
• Adding the dimensional data to the data mart
• Exercise – adding a few more hand-maintained dimensions
• Summary
• Chapter 8: Delivering Consistency in Your Data
• Technical requirements
• Keeping consistency by reusing code – macros
• Building on the shoulders of giants – dbt packages
• Summary
• Chapter 9: Delivering Reliability in Your Data
• Testing to provide reliability
• Testing the right things in the right places
• Summary
• Chapter 10: Agile Development
• Technical requirements
• Agile development and collaboration
• Applying agile to data engineering
• Building reports in an agile way
• Summary
• Chapter 11: Team Collaboration
• Enabling collaboration
• Working with branches and PRs
• Working with Git in dbt Cloud
• Making your first PR
• Summary
• Further reading
• Part 3: Hands-On Best Practices for Simple, Future-Proof Data Platforms
• Chapter 12: Deployment, Execution, and Documentation Automation
• Technical requirements
• Designing your deployment automation
• Advanced automation – hooks and run-operations
• Documentation
• Summary
• Chapter 13: Moving Beyond the Basics
• Technical requirements
• Building for modularity
• Managing identity
• Master Data management
• Saving history at scale
• Summary
• Chapter 14: Enhancing Software Quality
• Technical requirements
• Refactoring and evolving models
• Implementing real-world code and business rules
• Publishing dependable datasets
• Summary
• Further reading
• Chapter 15: Patterns for Frequent Use Cases
• Technical requirements
• Ingestion patterns
• History patterns
• Summary
• Further reading
• Index
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