Background: 

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. 

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. 

Challenges: 

The organization faced several pressing data environment challenges: 

• Legacy Architecture Complexity: The hybrid Data Warehouse + Data Lake architecture was highly customized and resource-intensive. 

• Data Latency & Redundancy: Data pipelines were slow, redundant, and unreliable, delaying analytics outcomes. 

• Operational Inefficiencies: Ineffective operations support and change management limited agility and slowed transformation. 

• Platform Instability: Recurring Cloudera outages posed risks to business continuity and insight delivery. 

• High Total Cost of Ownership (TCO): Ongoing maintenance and upgrades to the existing ecosystem were cost-prohibitive. 

• AI Enablement Gaps: Lack of standardized pipelines and scalable data infrastructure blocked enterprise AI initiatives. 

Solution: 

Narwal collaborated with the organization on a comprehensive data modernization initiative focused on agility, resilience, and future readiness. 

Framework-Based Reusability: 

• Developed a modular, reusable data modernization framework to reduce rework and accelerate delivery. 

• Automated data ingestion and transformation for structured and semi-structured sources. 

• Centralized governance of metadata, schema, and lineage to build data trust. 

Cloud-Native Architecture Transformation: 

• Migrated from Cloudera to a modern AWS + Snowflake architecture. 

• 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. 

• Rationalized BI artifacts, streamlined marts, and optimized data warehouse design. 

• Enhanced platform flexibility and reliability using scalable cloud services. 

CI/CD and Operational Automation: 

• Built automated CI/CD pipelines with robust scheduling and monitoring. 

• Enabled governance, scalability, and change control throughout the modernization journey. 

AI Readiness & Strategic Alignment: 

• Standardized data models and unified access layers to support downstream AI use cases. 

• Enabled real-time data flows for faster insights in supply chain and procurement. 

• Aligned the data platform with Smart Factory, IoT, and end-to-end supply chain digital strategies. 

Outcomes: 

The modernization initiative led to impactful, measurable results: 

• $350K+ in Annual Savings: Reduced infrastructure and licensing costs by replacing legacy DW + DL systems. 

• 30% Platform Performance Improvement and 20% improvement in SLA adherence. 

• 50% Reduction in Cost and Time through reusable frameworks and cloud-native automation. 

• Improved Stability and Uptime: Eliminated frequent outages, improving data availability SLAs. 

• Faster Analytics Cycles: Reduced latency, enabling quicker reporting and insight generation. 

• Scalable and Future-Ready: Platform designed to grow with business needs and AI innovation demands. 

• AI Enablement: Created a clean, trusted, and standardized data layer to support enterprise AI use cases. 

Conclusion: 

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. 

Let Narwal help you modernize your data environment and unlock the full potential of enterprise intelligence.