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Databricks Software / SaaS India

InMobi

Data Lake / Data Warehouse Modernization · Marketing Personalization · Real-time Analytics

InMobi needed to replace an expensive multicloud data warehouse because rising data volumes, siloed teams, and limited openness were hurting reporting performance and product agility.

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15%
20%
32%
Driving meaningful connections between customers and brands 32%

Reported impact

Business outcome

InMobi reported Driving meaningful connections between customers and brands 32%, Lower TCO compared to their multicloud data warehouse 15%, and Faster queries compared to their multicloud data warehouse 20%, improving mobile advertising analytics and personalization with measurable gains in cost reduction, productivity improvement, customer experience, and speed to market.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

InMobi uses Databricks Data + AI Platform, Databricks SQL, Unity Catalog, and MLflow to support mobile advertising analytics and personalization. InMobi needed to replace an expensive multicloud data warehouse because rising data volumes, siloed teams, and limited openness were hurting reporting performance and product agility. In practice, the implementation governs access and lineage with Unity Catalog, runs transformation or AI logic on Databricks Data + AI Platform, and delivers outputs through Databricks SQL. Customer, product, and interaction data are consolidated into a governed cloud data layer; Databricks Data + AI Platform applies analytics or model logic; results are surfaced through Databricks SQL. The case study reports Driving meaningful connections between customers and brands 32%, Lower TCO compared to their multicloud data warehouse 15%, and Faster queries compared to their multicloud data warehouse 20%, showing measurable improvement after the rollout.

Data flow

Customer, product, and interaction data are consolidated into a governed cloud data layer; Databricks Data + AI Platform applies analytics or model logic; results are surfaced through Databricks SQL.

Solution architecture

5 components · 3 layers
  1. Compute
    • Databricks Data + AI Platform Unifies engineering, analytics, and ai processing on one governed platform
    • MLflow Tracks experiments and model lifecycle management for the deployed ai workloads
  2. Serving
    • Databricks SQL Serves dashboards, reporting, and business-facing analytics on top of governed data
    • Power BI Serves dashboards, reporting, and business-facing analytics on top of governed data
  3. 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.
  • Uses serverless or elastic compute so capacity can scale up and down with workload demand.

Evidence from the source

15%
32%
Driving meaningful connections between customers and brands 32%
Faster queries compared to their multicloud data warehouse 20%
Lower TCO compared to their multicloud data warehouse 15%
Transparency Evidence & methodology 95/100 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 value95/100
  • 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
    6 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
    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

    InMobi customer story

    First-party
    https://www.databricks.com/customers/inmobi
    Source type
    Vendor-published customer story
    Published
    Jun 13, 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 Jun 13, 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.

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