About Narwal

AI-first IT services company serving 50+ clients, with over 500 projects delivered and a 98% satisfaction rate (4/4 NPS). We provide customized, scalable solutions across AI, Data, and Quality Engineering, enabling businesses to innovate faster and operate more efficiently.

Narwal specializes in AI, Data, and Quality Engineering, delivering innovative software solutions that enhance user experience and drive growth.

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Modernizing Data Platforms: Driving Agility and AI Readiness for a Leading U.S. Manufacturer 

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. 

Request a Consultation session Today!

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