Data Vault Training in Copenhagen, Denmark

Data Vault training focuses on a methodology for building scalable and flexible data warehouses. It teaches professionals how to create data models that can easily adapt to changing business requirements and integrate diverse data sources. The training covers the concepts of hubs, links, and satellites to design a robust data architecture. Participants learn to implement and manage Data Vault systems that support high-performance data integration and historical tracking. With Data Vault training, professionals gain the expertise to create agile and future-proof data solutions for organizations.

Introduction to Data Vault and Basic Components

• Introduction to Data Vault:

• What is Data Vault?

• History and evolution of Data Vault methodology.

• Benefits and challenges of using Data Vault in data warehousing.

• Key differences between Data Vault and traditional modeling techniques (Star Schema, Snowflake Schema).

• Core Components of Data Vault:

• Hubs: The central business keys of the data model.

• Links: Relationships between business keys.

• Satellites: Descriptive information about the business keys and relationships.

• Hands-on Exercise

• Data Vault Design Principles:

• Agile and iterative nature of Data Vault.

• Scalability and flexibility in data warehousing.

• How Data Vault supports historical tracking of data.

• Hands-on Exercise

• Overview of Data Vault Architecture:

• Key features of a Data Vault architecture.

• Layers in Data Vault: Raw Data Layer, Business Data Layer, and Information Consumption Layer.

• Hands-on Exercise

Data Vault Modeling and ETL Processes

• Detailed Look at Hubs, Links, and Satellites:

• In-depth analysis of the Hubs, Links, and Satellites structures.

• How to identify business keys and map them into Hubs.

• Creating relationships and capturing historical data using Links and Satellites.

• Hands-on Exercise

• ETL in Data Vault:

• Overview of ETL processes in Data Vault design.

• How to handle the ingestion of raw data into the Data Vault system.

• Staging layer and its role in Data Vault architecture.

• Implementing Data Vault ETL: Techniques for loading Hubs, Links, and Satellites.

• Hands-on Exercise

• Handling Slowly Changing Dimensions (SCDs) in Data Vault:

• Understanding and managing changes in data over time.

• Approaches for handling SCDs in Data Vault architecture.

• Techniques for maintaining historical data integrity.

• Hands-on Exercise

• Data Vault vs. Traditional Data Warehousing:

• Comparing Data Vault with traditional data warehousing techniques (e.g., Kimball, Inmon).

• When and why to use Data Vault methodology over others.

• Benefits of Data Vault in agile, cloud-based, and hybrid environments.

Advanced Data Vault Concepts and Real-World Application

• Advanced Topics in Data Vault Modeling:

• Multiple Satellites for handling different data types and histories.

• Using reference data in Data Vault and creating reference tables.

• Data Vault 2.0: Additional capabilities, including business data and real-time data processing.

• Hands-on Exercise

• Data Vault Automation and Tools:

• Overview of tools and technologies for automating Data Vault processes.

• How to leverage ETL tools, scripting, and automation platforms in a Data Vault implementation.

• Hands-on Exercise

• Data Vault in the Cloud:

• Benefits and challenges of implementing Data Vault in cloud environments (e.g., AWS, Azure, Google Cloud).

• Architecting Data Vault for cloud scalability and cost optimization.

• Hands-on Exercise

• Real-World Use Cases and Industry Applications:

• Examples of Data Vault implementations in various industries (finance, healthcare, retail).

• Best practices for Data Vault implementation and deployment.

• Hands-on Exercise

• Final Assessment and Review:

• Review of key concepts covered in the course.

• Q&A and discussion of common challenges faced in Data Vault implementations.