Designing the structure for your data warehouse is a complex and challenging process. As businesses deal with a growing number of sources and types of information that they need to integrate, they need a data modeling strategy that provides them with flexibility and speed. Data Vault is an approach that allows for evolving a data model in place without requiring destructive transformations and massive up front design to answer valuable questions. In this episode Kent Graziano shares his journey with data vault, explains how it allows for an agile approach to data warehousing, and explains the core principles of how to use it. If you’re struggling with unwieldy dimensional models, slow moving projects, or challenges integrating new data sources then listen in on this conversation and then give data vault a try for yourself.
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- Your host is Tobias Macey and today I’m interviewing Kent Graziano about data vault modeling and the role that it plays in the current data landscape
- How did you get involved in the area of data management?
- Can you start by giving an overview of what data vault modeling is and how it differs from other approaches such as third normal form or the star/snowflake schema?
- What is the history of this approach and what limitations of alternate styles of modeling is it attempting to overcome?
- How did you first encounter this approach to data modeling and what is your motivation for dedicating so much time and energy to promoting it?
- What are some of the primary challenges associated with data modeling that contribute to the long lead times for data requests or outright project Datafailure?
- What are some of the foundational skills and knowledge that are necessary for effective modeling of data warehouses?
- How has the era of data lakes, unstructured/semi-structured data, and non-relational storage engines impacted the state of the art in data modeling?
- Is there any utility in data vault modeling in a data lake context (S3, Hadoop, etc.)?
- What are the steps for establishing and evolving a data vault model in an organization?
- How does that approach scale from one to many data sources and their varying lifecycles of schema changes and data loading?
- What are some of the changes in query structure that consumers of the model will need to plan for?
- Are there any performance or complexity impacts imposed by the data vault approach?
- Can you talk through the overall lifecycle of data in a data vault modeled warehouse?
- How does that compare to approaches such as audit/history tables in transaction databases or slowly changing dimensions in a star or snowflake model?
- What are some cases where a data vault approach doesn’t fit the needs of an organization or application?
- For listeners who want to learn more, what are some references or exercises that you recommend?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Data Vault Modeling
- Data Warrior Blog
- OLTP == On-Line Transaction Processing
- Data Warehouse
- Bill Inmon
- Claudia Imhoff
- Oracle DB
- Third Normal Form
- Star Schema
- Snowflake Schema
- Relational Theory
- Sixth Normal Form
- Pivot Table
- Dan Linstedt
- Ralph Kimball
- Agile Manifesto
- Schema On Read
- Data Lake
- Data Vault Conference
- ODS (Operational Data Store) Model
- Supercharge Your Data Warehouse (affiliate link)
- Building A Scalable Data Warehouse With Data Vault 2.0 (affiliate link)
- Data Model Resource Book (affiliate link)
- Data Warehouse Toolkit (affiliate link)
- Building The Data Warehouse (affiliate link)
- Dan Linstedt Blog
- Perforrmance G2
- Scale Free European Classes
- Certus Australian Classes
- Data Vault Builder
- Varigence BimlFlex
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