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Databricks Retail Brazil

Grupo Casas Bahia

Enterprise Knowledge Search · Marketing Personalization

Grupo Casas Bahia, a leading Brazilian retailer, faced challenges with their outdated SAS infrastructure, hindering data-driven decision-making. Over time, however, reliance on a legacy SAS warehouse began to hinder their ability to scale and innovate.

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107 Nonengineering employees upskilled to transform data into insights
70% Cost reduction by migrating from SAS to Databricks
80–90% Automation of ETL scripts with Databricks Assistant

Reported impact

Business outcome

Reported outcomes include 70% Cost reduction by migrating from SAS to Databricks; 80–90% Automation of ETL scripts with Databricks Assistant; 107 Nonengineering employees upskilled to transform data into insights.

  • Cost Reduction

Solution

Grupo Casas Bahia, a leading Brazilian retailer, faced challenges with their outdated SAS infrastructure, hindering data-driven decision-making. By migrating to Databricks, the company streamlined operations and empowered users. Key improvements included automated SAS ETL script conversion with Databricks Assistant, intuitive data exploration with AI/BI Genie for all data users and robust data governance with Unity Catalog. This transition enabled personalized customer experiences and paved the way for future AI initiatives, enhancing Casas Bahia’s ability to serve millions of customers across online platforms and 800+ physical stores. These challenges extended to productivity and accessibility. The disconnected nature of the SAS environment forced manual workflows, making it harder for nontechnical users to explore and leverage data effectively. Over a decade of accumulated scripts, many without proper documentation, compounded the issue.

Data flow

Customer, product, and engagement data lands in Databricks, is transformed in the lakehouse under Unity Catalog governance, and is served to downstream users and applications through Databricks SQL, Genie, AI/BI Genie.

Solution architecture

5 components · 3 layers
  1. Compute
    • Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workloads.
  2. Serving
    • Genie Lets business users ask natural-language questions over governed enterprise data.
    • AI/BI Genie Lets business users ask natural-language questions over governed enterprise data.
    • Databricks SQL Serves dashboards, self-service queries, and business intelligence for downstream users.
  3. Governance
    • Unity Catalog Applies centralized governance, lineage, and access controls across shared data assets.

Architecture clues

  • Natural-language analytics is exposed through Genie.
  • Runs on Google Cloud.
  • Uses Unity Catalog for centralized governance and access control.

Evidence from the source

107 Nonengineering employees upskilled to transform data into insights
70% Cost reduction by migrating from SAS to Databricks
80–90% Automation of ETL scripts with Databricks Assistant
By migrating to Databricks, the company streamlined operations and empowered users.
Grupo Casas Bahia, a leading Brazilian retailer, faced challenges with their outdated SAS infrastructure, hindering data-driven decision-making.
Transparency Evidence & methodology 95/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Databricks

    Grupo Casas Bahia customer story

    First-party
    https://www.databricks.com/customers/grupo-casas-bahia
    Source type
    Vendor-published customer story
    Published
    Feb 3, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Feb 3, 2025 · Databricks · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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