Platform Engineering
From on-premise warehouse to a scalable, AI-ready cloud data platform
How a global luxury group migrated from an on-premise SAP data warehouse to a cloud-native platform on BigQuery and dbt, enabling self-service analytics with Looker.

A global luxury group operating in the luxury & retail sector, with more than 20,000 employees and operations across Europe.
Industry
Luxury & Retail
Location
Europe
Stack
BigQuery · dbt · Looker · GCP
Overview
Cloud-native platform built on BigQuery and dbt.
Data modeling standards and CI/CD from day one.
Governance and data quality practices implemented.
Background
The client
A global luxury group undergoing a multi-year data transformation, migrating from an on-premise data warehouse to Google Cloud to modernize analytics capabilities and empower business teams with reliable, self-service data.
The challenge
Moving SAP data to the cloud without the cloud expertise
The team was running a complex, on-premise SAP data environment and lacked the cloud skills needed to migrate confidently to GCP.
Beyond technical gaps, engaging business stakeholders in an IT-driven transformation proved difficult, and the data team needed a partner to ensure security, compliance, and cost control while delivering a strong user experience.
Solutions
Modernizing analytics with a cloud-native data foundation
01
Building a cloud-native data platform on GCP
Astrafy designed and built the data platform on GCP, leveraging BigQuery as the core analytical engine and dbt for robust, modular, and testable data transformations. Clear data modeling standards, CI/CD practices, and governance principles were established from day one to ensure data quality, lineage, and long-term maintainability.
02
Performance optimization and cost efficiency
Astrafy applied best practices for BigQuery performance and cost management, enabling scalable and sustainable analytics. The architecture was designed to accommodate growing data volumes while keeping infrastructure costs under control and ensuring a good experience for end users.
03
Upskilling the team for long-term autonomy
Throughout the engagement, the internal team was gradually upskilled on cloud-native tools and practices. A shared knowledge base and clear architecture gave everyone the confidence to work independently, accelerating the broader data transformation program.
Results
Turning AI into a scalable product
Internal team fully upskilled on cloud-native stack
Self-service data capabilities previously unavailable
Modular, scalable, and AI-ready platform delivered
Increased performance & reduced computing constraints
More data sources enabled for end-user consumption
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