RETAIL
Prepping for Scale: AI-Ready Data Platform
Serving up a modern, well-governed data platform on Databricks and dbt, cutting maintenance costs and opening up AI and ML work to teams across the company.
The client
The UK's market leader in subscription-based meal kits, delivering over 5 million meals a month. Data and AI sit at the centre of how the business runs, from optimising efficiency and forecasting demand to personalising recommendations for customers. Keeping that data foundation modern and reliable is essential to supporting business scale.
Key outcomes
Legacy Databricks setup migrated to Unity Catalog for cutting-edge capabilities
Refactoring for lower execution and maintenance costs
Extending dbt usage to support and improve ML and data science
Better governance and ways of working
The challenge
The client’s Databricks environment had been in place for some time and was still running on deprecated versions, which meant the team was missing out on newer features and capabilities.
Additionally, several data pipelines needed maintenance work to become more resilient and to keep pace with growing data volumes.
Some teams did leverage dbt and ML tools but there were no shared governance or standards in place, which made collaboration and scaling harder than it needed to be.
The Solution
Unity Catalog adoption
We migrated the client to Unity Catalog, giving the business a unified data catalogue with centralised governance and security. This also enabled secure sharing of data across environments and squads, and gave the team access to the latest Databricks capabilities.
Tech debt management
Legacy data pipelines were revamped to take advantage of these new capabilities, supporting better scaling and resilience going forward. This work also brought down maintenance and execution costs.
Empowerment and governance
We enhanced dbt use across the company and democratised access to data modelling and AI/ML work. Alongside this, we helped standardise ways of working, so teams could collaborate more consistently on data and ML projects.
The result
With Unity Catalog in place, the client now has a modern, well-governed data platform that supports secure collaboration across squads rather than isolated pockets of work.
The revamped pipelines are more resilient and cheaper to run, freeing up time previously spent on maintenance.
Wider, more consistent dbt adoption has also opened up data modelling and AI/ML work to more teams, giving the business a stronger foundation to keep optimising efficiency, forecasting demand, and personalising recommendations at scale.
Let’s walk the walk together
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