dbt for Data Transformation: Clean, Reliable Analytics Data Engineering with dbt · 4:20 Dive into the world of dbt (data build tool) and discover how it transforms raw, messy data into clean, reliable analytics-ready datasets using SQL-based models and built-in testing.
What is Analytics Engineering? Data Engineering with dbt · 3:21 Bridging the gap between raw data pipelines and business-ready insights, this chapter introduces analytics engineering as the crucial discipline that transforms messy data into clean, reliable models analysts can actually use.
Data Modeling Fundamentals Data Engineering with dbt · 4:36 Dive into the core building blocks of data organization, exploring how tables, rows, and columns work together to transform messy information into clean, structured datasets ready for analysis.
Dimensional Modeling Basics Data Engineering with dbt · 3:06 Dimensional Modeling Basics breaks down Ralph Kimball's foundational approach to data warehousing, teaching listeners how facts, dimensions, and star schemas work together to make business data simple, intuitive, and easy to analyze.
Introduction to dbt Data Engineering with dbt · 4:34 Discover how dbt (data build tool) bridges the gap between raw, messy data and meaningful insights by using SQL transformations to structure and prepare data for analysis. You'll learn how dbt combines the power of version control with SQL to create a reliable, step-by-step data transformation workflow.
Building dbt Models Data Engineering with dbt · 3:53 Dive into the core of dbt as you learn how to build models that transform raw warehouse tables into clean, structured data through SQL files, selections, joins, and filters.
dbt Testing and Quality Data Engineering with dbt · 3:24 Dive into the world of dbt testing and data quality, where you'll discover how to validate your data pipelines and ensure your numbers can actually be trusted before making critical decisions.
Data Documentation and Lineage Data Engineering with dbt · 7:04 Explore the critical practice of data documentation and lineage tracking, learning how to build systems that capture where data comes from, how it transforms, and where it flows — keeping your team aligned and your pipelines transparent.
Alternative Data Modeling Approaches Data Engineering with dbt · 3:04 A journey through the evolution of data modeling, from traditional star schemas to modern approaches like One Big Table (OBT), exploring how today's large-scale data demands are reshaping the way data engineers structure and query their models.
Semantic Layers and Metrics Data Engineering with dbt · 3:29 A deep dive into the chaos of conflicting data across teams, this chapter explores how semantic layers create a unified source of truth so everyone in an organization is working from the same definitions and metrics.
Reverse ETL Fundamentals Data Engineering with dbt · 3:32 Reverse ETL Fundamentals explores how to move processed data from your warehouse back into operational tools like CRMs and marketing platforms, bridging the gap between analytics and the business teams who need those insights to take action.
Data Observability Basics Data Engineering with dbt · 3:39 A deep dive into data observability, exploring the three essential pillars — Quality, Freshness, and Anomaly detection — that help data teams like Sarah's maintain confidence in their pipelines and catch problems before they impact reports and dashboards.