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Databricks Media and Entertainment South Korea

KRAFTON

Data Lake / Data Warehouse Modernization · Security and Compliance

KRAFTON needed to unify game and customer data across many studios because inconsistent access patterns and fragmented pipelines were slowing decision-making, collaboration, and privacy-aware innovation.

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60TB
Of streaming data processed each day 2,000
Watch video 20PB

Reported impact

Business outcome

KRAFTON reported Watch video 20PB, 60TB, and Of streaming data processed each day 2,000, improving governed game analytics and privacy-aware AI operations with measurable gains in productivity improvement, risk reduction, and scalability.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

KRAFTON uses Databricks Data + AI Platform and Unity Catalog to support governed game analytics and privacy-aware AI operations. KRAFTON needed to unify game and customer data across many studios because inconsistent access patterns and fragmented pipelines were slowing decision-making, collaboration, and privacy-aware innovation. In practice, the implementation governs access and lineage with Unity Catalog, and runs transformation or AI logic on Databricks Data + AI Platform. Legacy operational and analytics data are consolidated into a governed cloud data layer; Databricks Data + AI Platform applies analytics or model logic. The case study reports Watch video 20PB, 60TB, and Of streaming data processed each day 2,000, showing measurable improvement after the rollout.

Data flow

Legacy operational and analytics data are consolidated into a governed cloud data layer; Databricks Data + AI Platform applies analytics or model logic.

Solution architecture

2 components · 2 layers
  1. Compute
    • Databricks Data + AI Platform Unifies engineering, analytics, and ai processing on one governed platform
  2. Governance
    • Unity Catalog Enforces access controls, lineage, and governance across the data and models used in this workflow

Architecture clues

  • Centralized governance and lineage are enforced through Unity Catalog.
  • The platform is designed for near-real-time or streaming analysis rather than batch-only reporting.

Evidence from the source

60TB
Building a gaming experience that’s fun and secure
Of data from 40+ services managed successfully
Of streaming data processed each day 2,000
Watch video 20PB
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 impact25/25
    3 metrics describe measurable impact.
  • Deployment scale0/10
    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

    KRAFTON customer story

    First-party
    https://www.databricks.com/customers/krafton
    Source type
    Vendor-published customer story
    Published
    Sep 5, 2024
    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 Sep 5, 2024 · 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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