Case Study Lakehouse Architecture
Analysis should not start
with correcting data.
When each system generates data with its own logic, teams waste time cleaning, validating and correcting information before they can analyze the business.
Industry
Retail and ecommerce
-65%
Data processing time
↓ -65%
Data processing time.
↑ Quality
and consistency of information.
↑ +10 M
Integrated daily logs.
↑ 3x
Agility in delivering insights to business.
BEFORE
Data duplicated and spread across ERP, CRM, eCommerce, IoT and inventory.
Strategic reports take days to generate.
Teams spending more time cleaning and validating data than analyzing it.
NOW
Data organized in Bronze, Silver and Gold layers.
Automated cleaning, normalization and enrichment.
Reliable information ready for reporting, advanced analysis and 360º vision.
THE PROBLEM
The data are not ready to make decisions.
- Each system generates information with different structures and rules.
- Duplicated, fragmented and untraceable information.
- Teams have to review, clean and validate data before analyzing it.
- Management does not have a single, reliable view of the business.
THE SOLUTION
Development of Lakehouse Architecture with Medallion Model.
- Bronze: centralization of raw data from ERP, CRM, eCommerce, IoT and inventory.
- Silver: cleansing, normalization and enrichment through automated processes.
- Gold: creation of business models, metrics and KPIs ready for Power BI.
- Data Quality & Governance: rules to ensure consistency, integrity and traceability.
THE IMPACT
Less time preparing data. More time using reliable information.
The organization goes from redoing validations for each analysis to working on a common architecture that integrates, cleans and prepares the data from the source.
This allows for accelerated reporting, advanced analysis and 360º vision without duplicating processes.
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Transform your data platform
From raw data to decision-ready data
A layered architecture to integrate, clean and reuse information with quality, traceability and trust.
FAQs
Data architecture
What is a Medallion Lakehouse architecture?
A Medallion Lakehouse architecture organises data into progressive layers —Bronze, Silver and Gold— to move from raw data to analytics-ready data. The Bronze layer stores raw information, the Silver layer cleans and standardises it, and the Gold layer turns it into business models, metrics and KPIs ready for reporting, advanced analytics or Power BI.
Why do retail companies need a Lakehouse architecture?
Retail companies usually manage data from ERP, CRM, eCommerce, inventory, IoT and other systems. A Lakehouse architecture brings these sources into a shared data platform, reducing duplication, improving traceability and providing reliable data to analyse sales, operations, customers, stock and business performance.
What are the benefits of the Medallion model for retail analytics?
The Medallion model structures the data lifecycle from ingestion to business consumption. In retail analytics, it helps automate data cleansing, standardisation and enrichment, reduce preparation time and deliver insights to business teams faster.
How does a Lakehouse architecture improve data quality and consistency?
A Lakehouse architecture improves data quality and consistency by applying shared processes for integration, cleansing, validation and governance. This prevents teams from fixing data separately and creates a more reliable, traceable and reusable foundation for analytics.
How can a Lakehouse platform reduce reporting time?
A Lakehouse platform reduces reporting time by automating data preparation and making information available in analytics-ready layers. Instead of spending days cleaning, combining and validating data, teams can work with prepared models for dashboards, reporting and advanced analysis.