Databricks Energy Brazil

Sabesp

Data Lake / Data Warehouse Modernization · Fraud Detection · Risk Management

To support this transformation, Sabesp modernized its legacy Oracle Exadata-based data warehouse and adopted Databricks as the foundation for its data and AI strategy.

Today, Databricks powers a Data Mesh architecture spanning more than 23 business domains, supports large-scale SAP data ingestion, and enables self-service analytics and AI-driven applications across the organization.

Reported impact

Business outcome

Sabesp achieved measurable business impact on Databricks, including Today, Databricks powers a Data Mesh architecture spanning more than 23 business domains, supports large-scale SAP data ingestion, and enables self-service analytics and AI-driven applications across the organization..

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

As regulatory requirements evolved and customer expectations increased, the company embarked on a broad digital transformation to improve operational efficiency, strengthen customer experience, and enable faster, more informed decision-making across the business. To support this transformation, Sabesp modernized its legacy Oracle Exadata-based data warehouse and adopted Databricks as the foundation for its data and AI strategy. Today, Databricks powers a Data Mesh architecture spanning more than 23 business domains, supports large-scale SAP data ingestion, and enables self-service analytics and AI-driven applications across the organization. For Sabesp, data became a strategic asset in navigating this transformation.

Data flow

Unstructured documents and reference data lands in Databricks and is transformed into curated datasets under Unity Catalog governance before being served through Databricks SQL, AI/BI, Genie

Solution architecture

4 components · 2 layers
  1. Serving
    • Databricks SQL Serves dashboards, interactive analysis, and governed SQL workloads for business users at Sabesp.
    • Genie Lets nontechnical teams at Sabesp query governed data in natural language.
    • AI/BI Delivers business-facing dashboards and governed analytics for Sabesp.
  2. Governance
    • Unity Catalog Provides access control, governance, and lineage across Sabesp's shared data products and AI workloads.

Architecture clues

  • Cloud: Azure
  • Key product: AI/BI
  • Key product: Databricks SQL
  • Key product: Genie
  • Key product: Unity Catalog
  • Real-time or streaming data processing

Evidence from the source

As regulatory requirements evolved and customer expectations increased, the company embarked on a broad digital transformation to improve operational efficiency, strengthen customer experience, and enable faster, more informed decision-making across the business.
Sabesp (Companhia de Saneamento Básico do Estado de São Paulo) supplies drinking water and provides wastewater collection and treatment across 376 municipalities in the state of São Paulo serving around 30 million people.
Sabesp aims to leap five decades in five years, expanding access to safe water and sanitation for millions.
The water and sanitation industry in Brazil is undergoing significant change.
Today, Databricks powers a Data Mesh architecture spanning more than 23 business domains, supports large-scale SAP data ingestion, and enables self-service analytics and AI-driven applications across the organization.
Transparency Evidence & methodology 85/100 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 value85/100
  • 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 impact15/25
    1 metric 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

    Sabesp customer story

    First-party
    https://www.databricks.com/customers/sabesp
    Source type
    Vendor-published customer story
    Published
    Aug 18, 2026
    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 Aug 18, 2026 · 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.