About the company
Les Mills is a fitness and wellness company headquartered in Auckland, New Zealand. They have flagship fitness centers as well as a digital services line. Recently, leadership redirected the company towards a more modern, organized, and comprehensive data approach. They chose Snowflake and SqlDBM to be the cornerstone of their digital transformation because of the convenience and flexibility they offer. As they grow their data engineering team, they use SqlDBM to onboard new data hires. As they build new analytics initiatives in Snowflake, they use SqlDBM to standardize, document, and analyze those models.
How did SqlDBM help?
- Contextualize hanging tables through auto-suggested relationships
- Enrich data discovery and understanding through view lineage and relational diagrams
- Serve as an all-in-one tool for development, data discovery, governance, and CICD
Supporting global operations through a shared knowledge repository
Les Mills was looking for a modeling tool that could do more than just diagram Snowflake databases. It went beyond just reverse engineering. They needed a lightweight, collaborative tool that would serve as an all-in-one solution for development, data discovery, governance, and CICD. According to Craig Pyper, "Other tools like Erwin wouldn't cut it because it required us to compromise on usability and maintenance."
SqlDBM helped Craig's Data Platform team onboard data modelers, engineers, analysts, and governance personnel. With the click of a few buttons, the team was able to reverse engineer its Snowflake models and generate PK/FK relationships for a set of hanging tables and lineage for a set of views. Right away, SqlDBM reduced complexity, helping the company understand the relationships between database entities of all types.
Les Mills also established SqlDBM as its go-to documentation, and DevOps tool. The tool helps them achieve their goal of prioritizing data modeling within their development cycle by serving as their shared single-source of truth for all data models. It helps them standardize models, document them, analyze their lineage, and compare their versions. This helps Les Mills gain contextual clarity on their data assets.
Deploying to the database with SqlDBM's help
From a DevOps perspective, SqlDBM serves as the first step in Les Mills' deployment process. By using the revision comparison and live/local comparison features, the team was able to visualize the differences between different versions of their SqlDBM projects and their Snowflake database - and then generate the alter script. By using the Gitlab integration, they were then able to forward engineer their DDL revisions into their repository. From there, they have a manual deployment process, but could opt to substitute it with a deployment tool like Flyway or SchemaChange.
Empowering self-service data discovery
SqlDBM's Jira and Confluence integrations have also helped Craig's team improve their development speed and increase visibility across the organization. As they revise their models, they link comments to Jira tickets to track revisions and communication. When they're finished with the model, they publish it to Confluence so that non-modeling stakeholders can have easy access. This interconnected workspace is what drives efficiency for Craig's team.
Maintain visibility and data discovery
With Snowflake's scalable architecture and a growing data landscape, GreenLight's Data team can keep tabs on all their data assets and coordinate them using SqlDBM. Analysts and business users can now use the same models used to migrate and expand the business data model to find new data assets and relate them to existing datasets. In a few simple steps, a cloud-native tool like SqlDBM integrates with the other tools in GreenLight's data stack, such as git repositories, Confluence, and Jira-keeping the entire team coordinated and informed.