Let's Talk About Data Vault (w/ Brandon Taylor and Michael Olschimke)
Nov 17, 2023
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Brandon Taylor and Michael Olschimke discuss the Data Vault approach, its adoption in Europe, alignment with data mesh architecture, and the ongoing debate over Data Vault vs. Kimball methods. They also explore the impact of DataMesh on data warehouse design, recommended data modeling approaches, and hopes for the data industry.
Data Vault provides a structured approach for data management, enabling historical versioning and improved data governance.
Data Vault's flexibility makes it well-suited for modern data processing needs, addressing data privacy regulations and increasing demands for data lineage, security, and audibility.
Deep dives
Data Vault architecture and its benefits
The podcast episode explores the Data Vault architecture, a data modeling design pattern. Data Vault provides a structured approach that scales well to large teams, ensuring consistency in data management. It addresses common challenges such as reproducing historical reports and handling multiple source systems. By implementing Data Vault, organizations can achieve historical versioning, easily manage changes in source systems, and improve data governance.
The need for a standardized data management strategy
The episode discusses the importance of a standardized data management strategy. Creating a shared understanding of business rules, metrics, and data models is crucial to avoid miscommunication and inconsistencies in reporting. Data Vault provides a framework for standardizing the organization's data warehouse, ensuring that all teams are on the same page. This approach enables data literacy at scale and empowers business users to self-serve with consistent and reliable data.
Data Vault and the evolving data landscape
The conversation highlights the relevance of Data Vault in the context of evolving data requirements. Data Vault's flexibility and capacity to handle various data types, such as real-time, unstructured, and semi-structured data, make it well-suited for modern data processing needs. It is particularly valuable in addressing data privacy regulations like GDPR and increasing demands for data lineage, security, and audibility. Data Vault's ability to adapt to changing data landscapes positions it as a valuable data modeling methodology.
Considerations for adopting Data Vault
The episode advises considering factors such as the number of source systems and pain points in the current data solution when considering Data Vault adoption. While smaller organizations with fewer source systems can opt for simpler data modeling approaches like Kimball, companies with multiple source systems that face challenges in integration and data consistency can benefit from Data Vault. Data Vault adoption requires organizational buy-in, data literacy, and a willingness to embark on a data architecture transformation journey.
If Data Vault is a new term for you, it’s a data modeling design pattern. We’re joined by Brandon Taylor, a senior data architect at Guild, and Michael Olschimke, who is the CEO of Scalefree—the consulting firm whose co-founder Dan Lindstedt is credited as the designer of the data vault architecture.
In this conversation with Tristan and Julia, Michael and Brandon explore the Data Vault approach among data warehouse design methodologies. They discuss Data Vault’s adoption in Europe, its alignment with data mesh architecture, and the ongoing debate over Data Vault vs. Kimball methods.
For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.
The Analytics Engineering Podcast is sponsored by dbt Labs.
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