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Databricks Retail India

Myntra

Marketing Personalization · Real-time Analytics

Myntra needed to modernize its lakehouse because legacy architecture issues and slow pipelines limited its ability to serve personalized experiences at the scale of its fashion marketplace.

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25%
35%
Connecting people with brands that bring them joy 35%
Reduction in overall data infrastructure costs 25%

Reported impact

Business outcome

Myntra reported Connecting people with brands that bring them joy 35%, Reduction in overall data infrastructure costs 25%, and 35%, improving real-time shopping recommendations and retail pipelines with measurable gains in cost reduction, customer experience, and scalability.

  • Cost Reduction
  • Customer Experience
  • Scalability

Solution

Myntra uses Databricks Data + AI Platform, Delta Lake, and Apache Spark to support real-time shopping recommendations and retail pipelines. Myntra needed to modernize its lakehouse because legacy architecture issues and slow pipelines limited its ability to serve personalized experiences at the scale of its fashion marketplace. In practice, the implementation stores or organizes core datasets in Delta Lake, and runs transformation or AI logic on Databricks Data + AI Platform. Customer, product, and interaction data are consolidated into Delta Lake; Databricks Data + AI Platform applies analytics or model logic. The case study reports Connecting people with brands that bring them joy 35%, Reduction in overall data infrastructure costs 25%, and 35%, showing measurable improvement after the rollout.

Data flow

Customer, product, and interaction data are consolidated into Delta Lake; 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. Storage
    • Delta Lake Stores curated lakehouse tables used for analytics and machine learning

Architecture clues

  • The platform is designed for near-real-time or streaming analysis rather than batch-only reporting.
  • Uses a medallion-style data layering pattern to separate raw, cleaned, and refined data.

Evidence from the source

25%
35%
Connecting people with brands that bring them joy
Connecting people with brands that bring them joy 35%
Reduction in overall data infrastructure costs 25%
Transparency Evidence & methodology 90/100 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 value90/100
  • 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
    4 metrics describe measurable impact.
  • Deployment scale5/10
    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

    Myntra customer story

    First-party
    https://www.databricks.com/customers/myntra
    Source type
    Vendor-published customer story
    Published
    Jan 21, 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 Jan 21, 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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