Patrick Cuba: Data Mesh & Data Vault on Snowflake

Patrick Cuba: Data Mesh & Data Vault on Snowflake

In a recent webinar hosted by the Data Vault User Group, Patrick Cuba, "the Data Vault Guru", prolific blogger and seasoned Solutions Architect at Snowflake, shared his expertise on implementing Data Vault and integrating it with Data Mesh at a global software company. His presentation covered essential aspects of data architecture, automation, and best practices for data management. In this blog, we will look at Patrick's key points.

Background and Challenges

Patrick began by discussing the initial challenges faced by the company, which operates globally and needed a robust data management solution. Initially, they used on-premises SQL Server for their data warehouse, but as they grew, they migrated to AWS and Redshift. However, they encountered significant issues with Redshift, including high costs and long data processing times. Transition to Snowflake and Data Vault The company decided to transition to Snowflake due to its scalability and efficiency. They also adopted Data Vault to manage their data more effectively citing integration capabilities and agility as the main advantages over other approaches. Patrick stressed the need for a well-thought-out strategy when it comes to data architecture, highlighting the importance of careful planning and teamwork.

Implementation Process

Patrick detailed the implementation process, which involved using tools like dbt  and AutomateDV to automate the creation of Data Vault structures. This automation allowed the team to focus on business logic rather than technical details, significantly reducing development time and improving the consistency of the implementation across the team.

Integration with Data Mesh One of the main goals of the project was integrating Data Vault with Data Mesh principles. The company adopted a decentralised approach to data management, inspired by domain-driven design and team topology principles which guide Data Mesh. This approach allowed them to align teams with specific business domains. This allowed teams to make use of their specialised expertise for the given domain which led to improved data quality and a robust overall solution.

Practical Insights

Patrick shared several practical insights and observations from the project. Data Vault's structure facilitated easy tracking and storing of historical changes. The team followed Data Vault standards where hubs represent business keys, satellites store descriptive attributes, and links track relationships between hubs (entities). Using dbt and AutomateDV, the team automated much of the implementation of the Data Vault model, which improved onboarding and development time.

Benefits of Data Vault and Data Mesh

Since implementing Data Vault and integrating it with Data Mesh, the company has seen many benefits. A standardised approach to data modelling has led to more consistent and reliable data. The ability to easily add new data sources and business rules without major redesigns has enhanced scalability. The company can now respond faster to new requirements and troubleshoot more easily as the unified approach. The unified approach ensures the team has a shared understanding of the solution, improving collaboration and easing communication. More time is spent addressing business questions rather than managing data inconsistencies.

Future Directions

Looking ahead, the company plans to further enhance their data management capabilities by implementing data quality rules to ensure high data quality through automated testing and continuous integration. They are also focusing on performance optimisation to improve the throughput of data in their pipelines and creating targeted documentation for business users to better understand and make use of the data. The main focus of any project of this nature should always be business engagement and improving practices around the use of data to drive business growth; these initiatives are closely aligned with this goal.

Conclusion

Patrick's presentation highlighted the transformative impact of Data Vault and Data Mesh on the company's data management practices. By adopting a standardised, scalable, and flexible approach, they have significantly improved their ability to manage and utilise data effectively.

For a deeper dive into their journey and practical tips on implementing Data Vault and Data Mesh, watch the full webinar above.