# Analytics Engineering

> Learn analytics engineering by building. A hands-on education platform with a 10-module course (119 lessons), 1,604 graded SQL/Python/dbt exercises across 48 topics, 22 hands-on projects, 103 articles, and free SQL games. Created by Eric Provencio — analytics engineer at Disney, Hulu, Nike, Peloton, and Gopuff.

analyticsengineering.com teaches the modern data stack end to end: SQL, dimensional modeling, dbt, BigQuery, Snowflake, Looker, Python/pandas, and the analytics-engineering career path. Content is organized into topic hubs, a graded practice library, portfolio projects, and **Analytics Engineering Mastery** — a paid course with a BigQuery + dbt + Looker capstone.

Full URL index: [llms-full.txt](https://www.analyticsengineering.com/llms-full.txt)


## Getting started
- [Start here](https://www.analyticsengineering.com/start-here): Onboarding path for new visitors — what to read, practice, or buy first.
- [Intro quiz](https://www.analyticsengineering.com/intro-quiz): Short diagnostic to gauge SQL and analytics-engineering skill level.
- [Learning roadmap](https://www.analyticsengineering.com/roadmap): Personalized study plan built from hubs, practice topics, articles, and course modules.
- [Sample lesson](https://www.analyticsengineering.com/sample-lesson): Free preview of a course lesson.
- [Sample project](https://www.analyticsengineering.com/sample-project): Free preview of a portfolio project walkthrough.

## Flagship public work
- [Interactive SQL join visualizer](https://www.analyticsengineering.com/sql/joins): Inspect matched, unmatched, and multiplied rows.
- [Revenue analysis take-home](https://www.analyticsengineering.com/projects/take-home/revenue-fell-last-month): Versioned ecommerce case with a metric contract, rubric, and optional instructor reference.
- [Workforce employee attrition dataset](https://www.analyticsengineering.com/datasets/workforce-employee-attrition): Synthetic workforce files, documentation, and analysis paths.
- [Metric definition worksheet](https://www.analyticsengineering.com/templates/metric-definition-worksheet): Blank template, completed instructor example, and decision notes.
- [Analytics project README template](https://www.analyticsengineering.com/templates/project-readme): Blank template and completed project example.
- [dbt debugging project](https://www.analyticsengineering.com/projects/data-forge-the-lost-metrics): Recover lost metric definitions and model trust.

## Topic hubs
- [Analytics Engineering](https://www.analyticsengineering.com/analytics-engineering): What the role is, the modern data stack, and how the pieces fit together.
- [SQL for Analytics Engineers](https://www.analyticsengineering.com/sql): SELECT through window functions, CTEs, joins, and query optimization for analytics work.
- [dbt for Analytics Engineers](https://www.analyticsengineering.com/dbt): Models, tests, snapshots, macros, and dbt Cloud workflows.
- [Data Modeling](https://www.analyticsengineering.com/data-modeling): Star/snowflake schemas, fact and dimension tables, grain, SCD Type 2, and keys.
- [BigQuery for Analytics Engineers](https://www.analyticsengineering.com/bigquery): Partitioning, cost control, and SQL best practices on Google BigQuery.
- [Snowflake for Analytics Engineers](https://www.analyticsengineering.com/snowflake): Snowflake fundamentals for analytics engineers.
- [Looker & Looker Studio](https://www.analyticsengineering.com/looker): BI modeling, explores, and dashboard performance.
- [Python for Analytics Engineers](https://www.analyticsengineering.com/python): Python and pandas for data cleaning, analysis, and warehouse integration.
- [Analytics Engineering Career](https://www.analyticsengineering.com/career): Breaking in, role transitions, salary, and certifications.
- [Analytics Engineering Interview Prep](https://www.analyticsengineering.com/interview-prep): Technical screens, SQL questions, modeling rounds, and portfolio walkthroughs.

## Course & pricing
- [Analytics Engineering Mastery](https://www.analyticsengineering.com/course): Flagship course — 10 modules, 119 lessons, from SQL fundamentals through a BigQuery + dbt + Looker capstone.
- [Pricing](https://www.analyticsengineering.com/pricing): Course and membership tiers (free samples, Practice Pass, full course access).
- [Capstone project](https://www.analyticsengineering.com/capstone): End-to-end build — BigQuery warehouse, dbt models, GitHub workflow, and Looker Studio dashboard.

## Practice library
- [Practice library](https://www.analyticsengineering.com/practice): 1,604 graded exercises across 48 topics — SQL (26 topics), Python (15 topics), dbt/data-modeling/ETL quizzes, and storyline challenges.
- [Analytics engineering games](https://www.analyticsengineering.com/practice/games): Free timed and arcade quizzes using questions from the current exercise library.
- [SQL Rapid-Fire](https://www.analyticsengineering.com/practice/games/rapid-fire): Timed analytics-engineering quiz sprint with instant feedback.
- [SQL: Basic Select](https://www.analyticsengineering.com/practice/sql-basic-select): First SQL topic — free sample exercises available without an account.
- [SQL Quiz (200 Questions)](https://www.analyticsengineering.com/practice/sql-quiz-200-questions): Comprehensive SQL multiple-choice assessment.
- [Python Quiz (200 Questions)](https://www.analyticsengineering.com/practice/python-quiz-200-questions): Comprehensive Python multiple-choice assessment.
- [dbt Quiz (100 Questions)](https://www.analyticsengineering.com/practice/dbt-quiz-100-questions): dbt concepts and workflow quiz.
- [Data Modeling Quiz (100 Questions)](https://www.analyticsengineering.com/practice/data-modeling-quiz-100-questions): Dimensional modeling and architecture quiz.

## Portfolio projects
- [Portfolio projects](https://www.analyticsengineering.com/projects): 22 end-to-end builds for your analytics-engineering portfolio.
- [Portfolio guide](https://www.analyticsengineering.com/projects/portfolio): How to present projects in interviews and on GitHub.
- [Sports Equipment Pro Shop](https://www.analyticsengineering.com/projects/sports-equipment-pro-shop): Flagship e-commerce data modeling project.
- [Data Forge: The Lost Metrics](https://www.analyticsengineering.com/projects/data-forge-the-lost-metrics): dbt + BigQuery capstone-style build.
- [Data Modeling: Pizza Planet Party](https://www.analyticsengineering.com/projects/data-modeling-pizza-planet-party): Beginner-friendly dimensional modeling scenario.
- [Farm Data Harvest: ETL vs ELT](https://www.analyticsengineering.com/projects/farm-data-harvest-understanding-etl-vs-elt): Hands-on ETL vs ELT comparison project.

## Key articles
- [What is Analytics Engineering?](https://www.analyticsengineering.com/resources/what-is-analytics-engineering): Plain-language definition of the role and modern data stack.
- [What is dbt (Data Build Tool)?](https://www.analyticsengineering.com/resources/what-is-dbt-data-build-tool-a-simple-explanation): Intro to dbt models, tests, and documentation.
- [Explaining Fact and Dimension Tables for Beginners](https://www.analyticsengineering.com/resources/explaining-fact-and-dimension-tables-for-beginners): Core dimensional modeling concepts with examples.
- [Slowly Changing Dimensions Type 2 Explained](https://www.analyticsengineering.com/resources/slowly-changing-dimensions-type-2-explained): SCD Type 2 patterns and when to use them.
- [ETL vs ELT Explained Simply](https://www.analyticsengineering.com/resources/etl-vs-elt-explained-simply): Pipeline architecture trade-offs for analytics teams.
- [Mastering SQL: A Comprehensive Tutorial](https://www.analyticsengineering.com/resources/mastering-sql-a-comprehensive-tutorial): Full SQL reference for analytics work.
- [SQL Window Functions Explained With Examples](https://www.analyticsengineering.com/resources/sql-window-functions-explained-with-examples): ROW_NUMBER, RANK, LAG/LEAD, and running aggregates.
- [Best Practices for Data Documentation](https://www.analyticsengineering.com/resources/best-practices-for-data-documentation): How to document models, metrics, and lineage.
- [What Is a Lakehouse? Architecture & Use Cases](https://www.analyticsengineering.com/resources/what-is-a-lakehouse-architecture-use-cases): Lakehouse architecture overview.
- [dbt Cloud vs Core: Feature Comparison 2025](https://www.analyticsengineering.com/resources/dbt-cloud-vs-core-feature-comparison-2025): When to use dbt Cloud vs open-source Core.
- [BigQuery SQL Best Practices for Analysts](https://www.analyticsengineering.com/resources/bigquery-sql-best-practices-for-analysts): Cost and performance tips on BigQuery.
- [A Beginner's Guide to Snowflake for Analytics Engineers](https://www.analyticsengineering.com/resources/a-beginners-guide-to-snowflake-for-analytics-engineers): Snowflake setup and SQL for analytics engineers.
- [How to Become an Analytics Engineer Without a CS Degree](https://www.analyticsengineering.com/resources/how-to-become-an-analytics-engineer-without-a-cs-degree): Career transition playbook.
- [Interview Prep: 50 Questions and Answers](https://www.analyticsengineering.com/resources/interview-prep-50-questions-and-answers-for-analytics-engineer-roles): Common analytics-engineering interview questions.
- [All resources](https://www.analyticsengineering.com/resources): Full article library on SQL, dbt, modeling, career, BI, and architecture.

## Guides & comparisons
- [Guides](https://www.analyticsengineering.com/guides): Long-form guides for deeper study.
- [Analytics Engineering: The Complete 2026 Guide](https://www.analyticsengineering.com/guides/analytics-engineering): Flagship long-form reference on the role, skill stack, 2026 salary bands, interview loop, and three paths from zero to hired. The /analytics-engineering hub remains the curated learning path for the topic.
- [The Analytics Engineering Interview Kit](https://www.analyticsengineering.com/guides/analytics-engineering-interview-kit): SQL, modeling, portfolio, and behavioral prep for analytics-engineering screens.
- [Compare tools & roles](https://www.analyticsengineering.com/compare): Side-by-side comparisons of warehouses, frameworks, and career paths.
- [BigQuery vs Snowflake](https://www.analyticsengineering.com/compare/bigquery-vs-snowflake): Architecture, pricing, and when to choose each warehouse.
- [Analytics Engineer vs Data Engineer](https://www.analyticsengineering.com/compare/analytics-engineer-vs-data-engineer): Role boundaries, skills, and career paths.

## Glossary
- [Glossary](https://www.analyticsengineering.com/glossary): 16 analytics-engineering terms with definitions, examples, and related resources.
- [Analytics engineer](https://www.analyticsengineering.com/glossary/analytics-engineer): Role definition — transformation layer between data engineering and analysis.
- [Fact table](https://www.analyticsengineering.com/glossary/fact-table): Central measurable events table in a star schema.
- [Star schema](https://www.analyticsengineering.com/glossary/star-schema): Dimensional modeling pattern with a central fact and surrounding dimensions.
- [dbt model](https://www.analyticsengineering.com/glossary/dbt-model): Version-controlled SQL transformation in dbt.
- [Window function](https://www.analyticsengineering.com/glossary/window-function): SQL analytic functions for rankings, running totals, and period comparisons.

## Optional
- [About Eric Provencio](https://www.analyticsengineering.com/about): Author background and teaching approach.
- [Coaching](https://www.analyticsengineering.com/coaching): 1:1 analytics-engineering coaching sessions.
- [Webinar](https://www.analyticsengineering.com/webinar): Live training and Q&A sessions.
- [Terms of service](https://www.analyticsengineering.com/terms): Site terms.
- [Privacy policy](https://www.analyticsengineering.com/privacy): Privacy and data handling.
- [Sitemap](https://www.analyticsengineering.com/sitemap.xml): Machine-readable index of all public URLs.