Databricks Media and Entertainment Global

Bally's

Marketing Personalization · Real-time Analytics · Security and Compliance

Bally's needed a better way to support omnichannel gaming personalization, compliant analytics, and faster market launches because legacy technology fragmented customer and operational data, which slowed experimentation, delayed product launches, and limited self-service decision making.

10x serverless compute
30-40% player needs
60–70% building a scalable foundation for growth

Reported impact

Business outcome

Bally's improved omnichannel gaming personalization, compliant analytics, and faster market launches with measurable results including 60–70% building a scalable foundation for growth, 30-40% player needs, and 10x serverless compute, while giving teams a faster path from governed data to production decisions.

  • Compliance
  • Cost Reduction
  • Customer Experience
  • Speed to Market

Solution

Bally's uses Databricks Data + AI Platform to support omnichannel gaming personalization, compliant analytics, and faster market launches. The implementation replaced a legacy state where legacy technology fragmented customer and operational data, which slowed experimentation, delayed product launches, and limited self-service decision making. Player activity data and retail casino data move through Lakeflow Jobs and Structured Streaming and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. Lakehouse Federation and Serverless compute operationalize models, agents, and analytics that deliver personalized player experiences and self-service data products.

Data flow

Player activity data and retail casino data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI or analytics services deliver personalized player experiences and self-service data products.

Solution architecture

7 components · 6 layers
  1. Ingest
    • Structured Streaming Structured Streaming keeps player activity data and retail casino data available with low latency for downstream decisions.
  2. Compute
    • Databricks Data + AI Platform Databricks Data + AI Platform unifies engineering, analytics, governance, and AI workloads for omnichannel gaming personalization, compliant analytics, and faster market launches.
    • Serverless compute Serverless compute scales bursty data and model workloads behind omnichannel gaming personalization, compliant analytics, and faster market launches without fixed infrastructure overhead.
  3. Storage
    • Delta Lake Delta Lake stores curated player activity data and retail casino data so every team works from one trusted data foundation.
  4. Serving
    • Lakehouse Federation Lakehouse Federation lets teams query external systems tied to omnichannel gaming personalization, compliant analytics, and faster market launches without copying data into new silos.
  5. Orchestration
    • Lakeflow Jobs Lakeflow Jobs schedules ingestion and transformation of player activity data and retail casino data into reusable datasets.
  6. Governance
    • Unity Catalog Unity Catalog applies centralized access controls, lineage, and auditability across sensitive datasets.

Architecture clues

  • 30-40% player needs
  • 60–70% building a scalable foundation for growth
  • 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.

Evidence from the source

Our goal is to make data a capability that every part of the business can leverage, rather than something owned exclusively by technology teams,
That gives us governance and auditability at a level that would have been very difficult to achieve with legacy solutions.
The business was moving faster than our operating model could support, which is what ultimately led us to build a self-service platform that enables teams across the organization to operate with greater autonomy.
We now operate from a single data foundation with consistent governance across the enterprise,
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
    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
    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

    Bally's customer story

    First-party
    https://www.databricks.com/customers/ballys
    Source type
    Vendor-published customer story
    Published
    Jul 7, 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 Jul 7, 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.