Background:Â
A leading U.S.-based manufacturer, recognized as the largest independent processor of flat-rolled steel and a top supplier of industrial cylinders and pressure vessels faced critical data infrastructure challenges across its $2.89B enterprise. Operating with a highly customized legacy BI/DW ecosystem and a Cloudera-based data lake processing machine, sensor, and shop floor data, the company experienced growing instability, inefficiency, and poor scalability.Â
To address operational complexity and enable enterprise-wide AI readiness, the organization partnered with Narwal to modernize its data platform and reduce long-term TCO.Â
Challenges:Â
The organization faced several pressing data environment challenges:Â
- Legacy Architecture Complexity: The hybrid Data Warehouse + Data Lake architecture was highly customized and resource-intensive.Â
- Data Latency & Redundancy: Data pipelines were slow, redundant, and unreliable, delaying analytics outcomes.Â
- Operational Inefficiencies: Ineffective operations support and change management limited agility and slowed transformation.Â
- Platform Instability: Recurring Cloudera outages posed risks to business continuity and insight delivery.Â
- High Total Cost of Ownership (TCO): Ongoing maintenance and upgrades to the existing ecosystem were cost-prohibitive.Â
- AI Enablement Gaps: Lack of standardized pipelines and scalable data infrastructure blocked enterprise AI initiatives.Â
Solution:Â
Narwal collaborated with the organization on a comprehensive data modernization initiative focused on agility, resilience, and future readiness.Â
Framework-Based Reusability:Â
- Developed a modular, reusable data modernization framework to reduce rework and accelerate delivery.Â
- Automated data ingestion and transformation for structured and semi-structured sources.Â
- Centralized governance of metadata, schema, and lineage to build data trust.Â
Cloud-Native Architecture Transformation:Â
- Migrated from Cloudera to a modern AWS + Snowflake architecture.Â
- Migrated 54 dimension tables and 60 fact tables from the Data Warehouse, and 274 base tables with 73 views from the Data Lake impacting over 800 business users.Â
- Rationalized BI artifacts, streamlined marts, and optimized data warehouse design.Â
- Enhanced platform flexibility and reliability using scalable cloud services.Â
CI/CD and Operational Automation:Â
- Built automated CI/CD pipelines with robust scheduling and monitoring.Â
- Enabled governance, scalability, and change control throughout the modernization journey.Â
AI Readiness & Strategic Alignment:Â
- Standardized data models and unified access layers to support downstream AI use cases.Â
- Enabled real-time data flows for faster insights in supply chain and procurement.Â
- Aligned the data platform with Smart Factory, IoT, and end-to-end supply chain digital strategies.Â
Outcomes:Â
The modernization initiative led to impactful, measurable results:Â
- $350K+ in Annual Savings: Reduced infrastructure and licensing costs by replacing legacy DW + DL systems.Â
- 30% Platform Performance Improvement and 20% improvement in SLA adherence.Â
- 50% Reduction in Cost and Time through reusable frameworks and cloud-native automation.Â
- Improved Stability and Uptime: Eliminated frequent outages, improving data availability SLAs.Â
- Faster Analytics Cycles: Reduced latency, enabling quicker reporting and insight generation.Â
- Scalable and Future-Ready: Platform designed to grow with business needs and AI innovation demands.Â
- AI Enablement: Created a clean, trusted, and standardized data layer to support enterprise AI use cases.Â
Conclusion:Â
Narwal’s engagement transformed a fragmented, unstable data landscape into a streamlined, scalable, and AI-ready platform for one of America’s leading manufacturers. With reusable frameworks, modern architecture, and strong governance, the organization is now better equipped to drive innovation, efficiency, and long-term value.Â
Let Narwal help you modernize your data environment and unlock the full potential of enterprise intelligence.Â



