Use Cases
Telecom Providers
Enterprise Data Platform Migration to Databricks
Upgrading from 1.0 to 2.1
$50 000
Infrastructure Cost savings year on year
50%
Duplicate Data deletion
200
Stored procedures converted to Spark Applications
Use Case
Migration of Data Pipelines
Geography
Nordic Region
Period
18 Months
Problem
Modernization of Enterprise Data Platform
Industry
Logistics
Business Challenge
- Over 200 stored procedures with redundant logic
- Siloed platform with inefficient data processing
- High maintenance and operational overhead
- Lack of governance increasing complexity
- Rising infrastructure and management costs
Impact
- Implemented unified Databricks Lakehouse architecture
- Improved scalability and pipeline performance
- Enabled centralized governance and control
- Delivered cost-efficient data and analytics workloads
Before Diggibyte
- Legacy data warehouse with limited scalability
- Pipeline performance bottlenecks
- Lack of governance across data workflows
- Difficult to support growing data needs
After Diggibyte
- Predictive forecasting with 25% higher accuracy
- Automated pipelines and ML workflows
- Centralized data, governance and tracking
- Scalable, reusable and enterprise-ready
- Proactive insights and data-driven decisions
One of India’s Leading
Healthcare Administrators
100%
Legacy transformation logic was successfully migrated
60%
Reduction in platform evaluation time
40%
Improvement in data processing performance
Use Case
Migrating Legacy Data to Databricks
Geography
India
Period
12 Months
Problem
Healthcare
Industry
Modernizing Legacy Data Workflows
Business Challenge
- Need to evaluate Databricks using real business data and transformations
- Organization required practical validation instead of theoretical assessment
- Needed to assess both data engineering and reporting capabilities
- Evaluation had to be completed within a short trial window
- Required minimizing adoption risk before full-scale implementation
Impact
- Reduced adoption risk through a practical end-to-end proof of concept
- Validated Databricks scalability and performance using real transformation logic
- Enabled reporting through PySpark workflows and dashboards
- Supported faster business decision-making
- Established a strong foundation for enterprise data modernization
- Demonstrated a scalable and future-ready analytics approach
Before Diggibyte
- Data processing relied on legacy approaches with limited scalability and efficiency
- No hands-on experience with Databricks or PySpark
- Reporting workflows were manual or tightly coupled with outdated systems
- Uncertainty existed around performance, usability, and modernization capabilities
After Diggibyte
- Existing data transformation logic migrated to PySpark
- Workloads executed successfully on the Databricks trial environment
- Processed data made available through dashboards
- Stakeholders could visually validate outputs and reporting capabilities
- End-to-end implementation demonstrated Databricks suitability for scalable analytics workloads
India’s Leading
Digital-First Jeweler
Enabling Scalable, Governed & AI-Powered Analytics with Databricks Lakehouse
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
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
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
One of India’s Leading
AI-Powered Decision Intelligence Platform
50%
Reduction in processing time
70%
Reduction in bad data propagation
100%
Automated schema drift detection
Use Case
Enabling Scalable Data Quality
Geography
India
Period
12 Months
Problem
GenAI
Industry
No Modern Data Platform
Business Challenge
- Absence of a modern data platform limited trust in data
- Analytics adoption was slowed
- Operational risks increased
- Business users lacked visibility into data quality
- Insights and decision-making were delayed
Impact
- Delivered an end-to-end production-ready architecture
- Built-in governance, data quality, and observability implemented
- Databricks validated as a scalable platform
- AI/BI dashboards enabled real-time visibility into data health
Before Diggibyte
- Data ingestion and processing lacked a unified platform
- No support for schema drift handling
- Automated data quality checks were absent
- No incremental processing capabilities
- Governance, lineage, and monitoring were missing
- Scaling was risky and manual
After Diggibyte
- Databricks-based Medallion architecture implemented
- Unity Catalog, DLT, PySpark, and Jobs integrated
- Governed ingestion and schema evolution enabled
- Automated data quality validation introduced
- Incremental processing with orchestration established
One of India’s Leading
Fintech Platform for Assisted Financial Sales
100%
Enablement in near–real-time data
40+
Migrated and optimized legacy Python scripts
50%
Improvement in ad hoc query performance
Use Case
Real-Time Lakehouse Platform
Geography
India
Period
12 Months
Problem
FinTech
Industry
Scalability and Cost Constraints
Business Challenge
- Increasing infrastructure costs directly impacted margins
- Slow analytics reduced agility in business decision-making
- Limited scalability constrained growth
- Lack of centralized governance introduced compliance and data access risks
Impact
- Diggibyte delivered a scalable, cost-efficient analytics foundation
- Migrated legacy workloads and 40+ Python scripts into optimized Databricks workflows
- Reduced latency and improved query performance
- Strengthened governance
- Positioned organization for high-growth, data-driven operations
Before Diggibyte
- Legacy analytics on Doris and Athena faced high costs, poor performance, and slow queries.
- Fragmented data processing
- Inefficient ETL workflows
- Limited governance control
After Diggibyte
- Implemented a unified Databricks Lakehouse architecture
- Leveraged Lakeflow pipelines for near real-time ingestion at five-minute intervals
- SQL Warehouses improved interactive analytics performance
- Unity Catalog centralized governance, security, and access control
Nordic’s Leading Logistics
Uncovering Legacy Warehouse Challenges
30%
Decreased IT costs year on year
80%
Data engineers time saved
25%
Growth on data adoption
Use Case
Legacy migration
Geography
Nordic Region
Period
18 Months
Problem
Logistics
Industry
The Shift from Legacy to Lakehouse"
Business Challenge
- Legacy systems carried inherent complexities and limitations.
- Existing warehouse architecture struggled to meet growing business demands.
- Initial pilot analysis identified performance bottlenecks in legacy warehouses.
- High maintenance costs and inefficient processing impacted operations.
- Legacy constraints reduced scalability and system flexibility.
Impact
- Adopted a “divide and conquer” strategy for system modernization.
- Built a unified global lakehouse architecture integrating diverse warehouses.
- Eliminated major data silos across the organization.
- Achieved significant improvements in data utilization and accessibility.
- Reduced maintenance efforts and operational overhead.
- Created a centralized repository for streamlined analytics and reporting.
Before Diggibyte
- Customer’s analytics landscape was burdened by 13 separate legacy data warehouse systems.
- Multiple systems created operational complexity in IT infrastructure.
- Increased operational and maintenance costs due to fragmented architecture.
- Extensive maintenance efforts were required to manage legacy systems.
After Diggibyte
- Implemented advanced Databricks and lakehouse architecture solutions.
- Established a strong foundation for streamlined migration processes.
- Improved organizational data utilization and accessibility.
- Achieved overall cost savings and operational efficiency.