Data Vault

Concepts covered: dmDataVault

Hubs, Links, and Satellites Data Vault is a modeling methodology designed for agility and auditability in enterprise data warehouses. It decomposes data into three types of tables: Hubs (business keys), Links (relationships between hubs), and Satellites (descriptive attributes with history). The structure is more complex than star schemas but handles change more gracefully. Hubs store only the business key (customer_id, product_sku) and a hash key. Links store the relationship between two hubs (customer_X_ordered_product_Y). Satellites store the descriptive attributes (customer_name, customer_address) with effective dates for full history. Data Vault separates structure (hubs and links) from content (satellites). Adding a new attribute means adding a satellite, not modifying the hub or lin

About This Interactive Section

This section is part of the Design Patterns lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.