Analytics Engineering with SQL and dbt

Spis treści ebooka

• Preface
  • Why We Wrote This Book
  • Who This Book Is For
  • How This Book Is Organized
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Analytics Engineering
  • Databases and Their Impact on Analytics Engineering
  • Cloud Computing and Its Impact on Analytics Engineering
  • The Data Analytics Lifecycle
  • The New Role of Analytics Engineer
  • Responsibilities of an Analytics Engineer
  • Enabling Analytics in a Data Mesh
    • Data Products
    • dbt as a Data Mesh Enabler
  • The Heart of Analytics Engineering
  • The Legacy Processes
    • Using SQL and Stored Procedures for ETL/ELT
    • Using ETL Tools
  • The dbt Revolution
  • Summary
• 2. Data Modeling for Analytics
  • A Brief on Data Modeling
    • The Conceptual Phase of Modeling
    • The Logical Phase of Modeling
    • The Physical Phase of Modeling
    • The Data Normalization Process
  • Dimensional Data Modeling
    • Modeling with the Star Schema
    • Modeling with the Snowflake Schema
    • Modeling with Data Vault
  • Monolith Data Modeling
  • Building Modular Data Models
    • Enabling Modular Data Models with dbt
      • Referencing data models
      • Staging data models
      • Base data models
      • Intermediate data models
      • Mart models
    • Testing Your Data Models
    • Generating Data Documentation
    • Debugging and Optimizing Data Models
  • Medallion Architecture Pattern
  • Summary
• 3. SQL for Analytics
  • The Resiliency of SQL
  • Database Fundamentals
    • Types of Databases
    • Database Management System
    • Speaking with a Database
  • Creating and Managing Your Data Structures with DDL
  • Manipulating Data with DML
    • Inserting Data with INSERT
    • Selecting Data with SELECT
      • Filtering data with WHERE
        • SQL operators
      • Aggregating data with GROUP BY
      • Sorting data with ORDER BY
      • Joining data with INNER, LEFT, RIGHT, FULL, and CROSS JOIN
        • INNER JOIN
        • LEFT JOIN (or LEFT OUTER JOIN)
        • RIGHT JOIN (or RIGHT OUTER JOIN)
        • FULL JOIN (or FULL OUTER JOIN)
        • CROSS JOIN
    • Updating Data with UPDATE
    • Deleting Data with DELETE
  • Storing Queries as Views
  • Common Table Expressions
  • Window Functions
  • SQL for Distributed Data Processing
    • Data Manipulation with DuckDB
      • Installing DuckDB
      • Running SQL queries with DuckDB
    • Data Manipulation with Polars
      • Installing Polars
      • Running SQL queries with Polars
    • Data Manipulation with FugueSQL
      • Installing Fugue and FugueSQL
      • Running SQL queries with FugueSQL
  • Bonus: Training Machine Learning Models with SQL
  • Summary
• 4. Data Transformation with dbt
  • dbt Design Philosophy
  • dbt Data Flow
  • dbt Cloud
    • Setting Up dbt Cloud with BigQuery and GitHub
    • Using the dbt Cloud UI
    • Using the dbt Cloud IDE
  • Structure of a dbt Project
    • Jaffle Shop Database
    • YAML Files
      • dbt_project.yml
      • packages.yml
      • profiles.yml
    • Models
    • Sources
      • Source freshness
    • Tests
      • Generic tests
      • Singular tests
      • Testing sources
    • Analyses
    • Seeds
    • Documentation
    • dbt Commands and Selection Syntax
      • dbt run
      • dbt test
      • dbt docs
      • dbt build
      • Other commands
      • Selection syntax
        • Wildcard *
        • Tags
        • Model name
        • Dependencies
        • Packages
        • Multiple selections
    • Jobs and Deployment
  • Summary
• 5. dbt Advanced Topics
  • Model Materializations
    • Tables, Views, and Ephemeral Models
    • Incremental Models
    • Materialized Views
    • Snapshots
  • Dynamic SQL with Jinja
  • Using SQL Macros
  • dbt Packages
    • Installing Packages
    • Exploring the dbt_utils Package
    • Using Packages Inside Macros and Models
  • dbt Semantic Layer
  • Summary
• 6. Building an End-to-End Analytics Engineering Use Case
  • Problem Definition: An Omnichannel Analytics Case
  • Operational Data Modeling
    • Conceptual Model
    • Logical Model
    • Physical Model
  • High-Level Data Architecture
  • Analytical Data Modeling
    • Identify the Business Processes
    • Identify Facts and Dimensions in the Dimensional Data Model
    • Identify the Attributes for Dimensions
    • Define the Granularity for Business Facts
  • Creating Our Data Warehouse with dbt
  • Tests, Documentation, and Deployment with dbt
  • Data Analytics with SQL
  • Conclusion
• Index

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