India’s Leading

Digital-First Jeweler

Enabling Scalable, Governed & AI-Powered Analytics with Databricks Lakehouse

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40%

Reduction of BI and platform-related costs

50%

Improvement in pipeline reliability and observability

70%

Enablement of self-service analytics adoption

Use Case

Databricks Lakehouse

Geography

India

Period

16 Months

Problem

Limited Scalability & Governance

Industry

Retail

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Business Challenge​

  • Increasing data complexity and infrastructure costs
  • Limited pipeline observability affecting reliability
  • High dependency on manual and custom development
  • Absence of native NLP capabilities
  • Lack of unified governance and lineage
  • Reduced data trust, compliance, and accessibility
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Impact

  • Unified Lakehouse reduced operational complexity
  • Significant reduction in BI and platform costs
  • Improved pipeline reliability and observability
  • Enabled self-service analytics with NLP chatbot
  • Strengthened governance, lineage, and compliance
  • Enhanced data trust and decision-making efficiency

Before Diggibyte

  • Limited scalability due to schema evolution challenges
  • Poor pipeline observability impacting reliability
  • High BI tool and platform costs
  • No native NLP → heavy SQL-based development
  • Gaps in governance and data lineage
  • Reduced data trust, compliance, and accessibility

After Diggibyte

  • Unified Databricks Lakehouse architecture
  • Scalable and observable data pipelines
  • Optimized BI and platform costs
  • Genie-powered NLP for self-service analytics
  • Centralized governance and lineage tracking
  • Improved trust, compliance, and usability