Use Case Intelligence RSS Search use cases Search
Databricks Telecommunications United States

AT&T

Data Lake / Data Warehouse Modernization · System Migration

AT&T needed a better way to support large-scale data lake migration and enterprise analytics modernization because an expensive on-premises Hadoop estate made it difficult to democratize data, scale AI workloads, and keep migration risk under control.

Read the original story Explore all use cases
3 acceleration in data science cycles
300% pursuing greater possibilities in the cloud
3x roi from migrating one data center to the cloud
AT&T cuts fraud by 80% with real-time AI on Databricks

Reported impact

Business outcome

AT&T improved large-scale data lake migration and enterprise analytics modernization with measurable results including 300% pursuing greater possibilities in the cloud, 3x roi from migrating one data center to the cloud, and 3 acceleration in data science cycles, while giving teams a faster path from governed data to production decisions.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

AT&T uses Databricks Data + AI Platform to support large-scale data lake migration and enterprise analytics modernization. The implementation replaced a legacy state where an expensive on-premises Hadoop estate made it difficult to democratize data, scale AI workloads, and keep migration risk under control. Network and customer data and legacy hadoop lake data move through Spark Declarative Pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. Databricks SQL and Agent Bricks operationalize models, agents, and analytics that deliver shared reports and dashboards and cloud-native data products.

Data flow

Network and customer data and legacy hadoop lake data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver shared reports and dashboards and cloud-native data products.

Solution architecture

6 components · 5 layers
  1. Ingest
    • Spark Declarative Pipelines Spark Declarative Pipelines standardizes network and customer data and legacy Hadoop lake data into production-grade tables and features.
  2. Compute
    • Databricks Data + AI Platform Databricks Data + AI Platform unifies engineering, analytics, governance, and AI workloads for large-scale data lake migration and enterprise analytics modernization.
  3. Storage
    • Delta Lake Delta Lake stores curated network and customer data and legacy Hadoop lake data so every team works from one trusted data foundation.
  4. Serving
    • Databricks SQL Databricks SQL serves curated dashboards and ad hoc analysis to shared reports and dashboards and cloud-native data products.
    • Agent Bricks Agent Bricks packages domain-specific agents that automate parts of large-scale data lake migration and enterprise analytics modernization.
  5. Governance
    • Unity Catalog Unity Catalog applies centralized access controls, lineage, and auditability across sensitive datasets.

Architecture clues

  • 300% pursuing greater possibilities in the cloud
  • Automated ingestion and transformation pipelines keep source data synchronized for downstream analytics and AI.
  • Centralized governance, lineage, and access controls are enforced through Unity Catalog.
  • Delta Lake provides the shared storage layer for curated lakehouse datasets.
  • Operational AI workflows are deployed directly beside governed data and analytics assets.

Evidence from the source

Moving to Azure Databricks has transformed the data culture at AT&T,
Our primary motivations for our cloud migration were scalability, elasticity, nimbleness, time to market, delivering additional value to our customers and reducing cost,
We had situations where different business leaders would report on the same metric, but each one was presenting slightly different numbers,
We were looking specifically for a solution that could support all the use cases we had been running on-premises up to that point,
Transparency Evidence & methodology 100/100 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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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
    5 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
    4 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

    AT&T customer story

    First-party
    https://www.databricks.com/customers/att-azure-databricks
    Source type
    Vendor-published customer story
    Published
    Oct 17, 2023
    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 Oct 17, 2023 · 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.

Related

Similar use cases

See all like this
Databricks Mar 30, 2026 BAYADA Home Health Care Healthcare United States BAYADA delivers clinical, assistive, and hospice services to tens of thousands of patients weekly, but its legacy landscape of multiple practice management systems, an on-prem ODS, Snowflake, and legacy cubes forced… 3 separate data platforms consolidated into one governed warehouse Data Lake / Data Warehouse ModernizationSystem Migration Databricks Asset Bundles · Databricks Assistant · Databricks Genie Databricks May 22, 2024 KPMG Commercial and Professional Services Global KPMG needed a better way to support enterprise data federation, governance, and analytics modernization because legacy Hadoop infrastructure and firmwide data silos made it difficult to serve analytics, machine… Data Lake / Data Warehouse ModernizationSecurity and Compliance Agent Bricks · Databricks Data + AI Platform · Databricks SQL Databricks Mar 24, 2025 NTT DOCOMO Telecommunications Japan However, the degree of utilization within the company was uneven, which impacted their ability to visualize the status of utilization and extract insights to enhance functionality and performance. As a result, NTT… 90% More efficient analysis of LLM usage Customer Service AutomationData Lake / Data Warehouse Modernization Agent Bricks · Databricks SQL · Genie