Data Vault: Building a Scalable Data Warehouse Training Course
Data Vault Modeling is a database modeling methodology that ensures long-term historical storage for data originating from various sources. A Data Vault maintains a single version of the facts, or "all the data, all the time". Its flexible, scalable, consistent, and adaptable design integrates the best features of 3rd normal form (3NF) and star schema.
In this instructor-led live training, participants will learn how to construct a Data Vault.
By the end of this training, participants will be able to:
- Understand the architecture and design principles behind Data Vault 2.0, as well as its integration with Big Data, NoSQL, and AI.
- Apply Data Vaulting techniques to facilitate auditing, tracing, and inspection of historical data within a data warehouse.
- Create a consistent and repeatable ETL (Extract, Transform, Load) process.
- Build and deploy highly scalable and repeatable warehouses.
Course Format
- A mix of lectures, discussions, exercises, and extensive hands-on practice.
Course Outline
Introduction
- Limitations of current data warehouse data modeling architectures.
- Benefits of Data Vault modeling.
Overview of Data Vault architecture and design principles.
- SEI / CMM / Compliance.
Data Vault applications.
- Dynamic Data Warehousing.
- Exploration Warehousing.
- In-Database Data Mining.
- Rapid Linking of External Information.
Data Vault components.
- Hubs, Links, and Satellites.
Building a Data Vault.
Modeling Hubs, Links, and Satellites.
Data Vault reference rules.
Interaction between components.
Modeling and populating a Data Vault.
Converting 3NF OLTP to a Data Vault Enterprise Data Warehouse (EDW).
Understanding load dates, end-dates, and join operations.
Business keys, relationships, link tables, and join techniques.
Query techniques.
Load processing and query processing.
Overview of the Matrix Methodology.
Ingesting data into data entities.
Loading Hub Entities.
Loading Link Entities.
Loading Satellites.
Using SEI/CMM Level 5 templates to achieve repeatable, reliable, and quantifiable results.
Developing a consistent and repeatable ETL (Extract, Transform, Load) process.
Building and deploying highly scalable and repeatable warehouses.
Closing remarks.
Requirements
- Knowledge of data warehousing concepts.
- Knowledge of database and data modeling concepts.
Audience
- Data modelers.
- Data warehousing specialists.
- Business Intelligence specialists.
- Data engineers.
- Database administrators.
Open Training Courses require 5+ participants.
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Course - Data Vault: Building a Scalable Data Warehouse
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