DataVault Training Course

Target Audience

  • Data Engineers, data analysts, and BI developers looking to master the Data Vault methodology for building corporate data warehouses (DWH).
  • Level: beginner and intermediate.

Course Goal

  • Learn the basics of the Data Vault methodology.
  • Understand how to build flexible, scalable, and easily maintainable data warehouses.
  • Master the tools and approaches used to implement Data Vault in practice.

Module 1: Introduction to Data Vault

1.1 History and principles of Data Vault

  • Evolution of data warehouses: from Inmon and Kimball to Data Vault.
  • Key principles: flexibility, scalability, auditability, and historicity.
  • When and why to choose Data Vault.

1.2. Data Vault Architecture

  • Logical and physical Data Vault model.
  • Comparison with traditional approaches (star and snowflake schemas).
  • Roles of components: hubs, links, satellites.

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Module 2: Data Vault Modeling Basics

2.1. Hubs

  • Definition, structure, and purpose.
  • How to select business keys.

2.2. Links

  • Modeling relationships between hubs.
  • Multiple and hierarchical relationships.

2.3. Satellites

  • Storing attributes and changes.
  • Managing data historicity.

2.4. Best Practices for Data Vault Modeling

  • Working with changing keys.
  • Performance optimization.
 

Module 3. Data Vault Implementation in Practice

3.1. Tools And Technologies

  • Choosing a DBMS: SQL Server, Snowflake, PostgreSQL, and others.
  • ETL/ELT tools: SSIS, Azure Data Factory, Apache Airflow.
  • Automating Data Vault: ready-made frameworks (e.g., dbt Vault).

3.2. Data Load Processing (ETL ELT))

  • Loading stages: Staging, Raw Vault, Business Vault.
  • Loading hubs, links, and satellites.

3.3. Data Quality Management

  • Data validation and verification mechanisms.
  • Managing errors and anomalies.

Module 4. Data Vault in Analytics

4.1 Building a Business Vault

  • Using aggregates and views.
  • Integrating business rules.

4.2. Generating Data Marts

  • Data Mart models: star and snowflake schemas.
  • Automating the creation of analytical marts.

4.3. Data Visualization

  • Integration with BI tools (Power BI, Tableau, Qlik).
  • Examples of dashboards based on Data Vault.

Module 5. Data Vault Administration and Optimization

5.1. Performance

  • Optimizing queries and table structures.
  • Indexing, partitioning, and data compression.

5.2. Metadata Management

  • Using metadata to automate processes.
  • Metadata management tools.

5.3. Archiving and deleting data

  • Strategies for managing "outdated" data.
  • Compliance with personal data policies and other standards.
 

Module 6. Practical part (Workshop)

6.1 Designing a Data Vault Modell

  • Working with a real-world case.
  • Creating hubs, links, and satellites.

6.2. Developing an ETL ELT process

  • Implementation on a chosen platform.

6.3. Building an analytical showcase

  • Example of creating a Data Mart and data visualization.
 

Conclusion

  • Recapping key points of the course.
  • Tips for further study and practice.
  • Summarizing and answering questions.

Course Format

  • Duration: 5 days (4 hours each) or 10 sessions of 2 hours.
  • Materials: presentations, practical tasks, code examples.
  • Format: online/offline with access to a lab environment.

Additionally

  • Checklist for implementing Data Vault in a company.
  • Recommendations for books, articles, and conferences.