Insights

Regional Financial Institution in Texas Revamps Data Workflows for Faster Delivery 

Author: Subbulakshmi Arunachalam

THE CHALLENGE 

A leading regional credit union in Texas relied on data from multiple sources, including CRM systems, file feeds, APIs, and GraphQL endpoints. Each new data source required manual pipeline development, slowing delivery and delaying downstream applications. 

Batch processing performance was poor, data quality issues limited the efficacy of analytics, and their architecture struggled to scale. Integration across legacy and cloud systems added further complexity. 

Key challenges included: 

• Manual pipeline development slowed the integration of new data sources 
• Poor interface performance during batch runs 
• Analytics was limited by data quality and reliability issues  
• Complex integration across legacy and modern systems 
• Limited scalability for future business needs 

A current client referred us to the credit union, and we were selected for the project because of our familiarity with the credit union’s tech stack and our track record. Our extensive experience with Microsoft Azure data platforms and our ability to deliver practical solutions helped us stand out from other competitors.  

THE SOLUTION 

We approached this engagement with a structured analysis of the client’s existing processes. After evaluating the bottlenecks, we delivered proof of concept that addressed both short-term performance gaps and long-term scalability. 

Phase 1: Assessment and Proof of Concept 

• Conducted a detailed analysis of the ingestion workflows and batch performance 
• Identified security, governance, and performance needs 
• Delivered a functional proof of concept demonstrating the benefits of metadata-driven pipelines 

Phase 2: Plan, Implement, and Validate Metadata-Based Framework 

We partnered with the client’s infrastructure and security teams to meet compliance and access requirements to: 

• Build a metadata-driven ETL framework using Azure Data Factory Pipelines 
• Simplify architecture for quick onboarding of new data sources with minimal code 
• Add role-based access controls to protect sensitive information and limit access to personally identifiable information 
• Integrate quality checks and reconciliation processes to ensure data integrity 
• Implement Microsoft Purview for data governance and DevOps integration 

This solution significantly reduced dependency on manual development while improving control, visibility, and performance across the data pipeline. 

THE RESULTS 

“We focused on reducing the effort, time, and complexity it took to bring new data online. By building a flexible framework, we helped the client gain more control, better data quality, and time savings to shift their data engineering team’s attention to higher-value work.” 

Subbulakshmi Arunachalam, Cloud Data Architect, Beyondsoft 

Improved Time-to-Market 

• 90% faster turnaround in delivering data from source to target 
• On-premises to cloud integration completed in half the time  

Developer Productivity 

Performance and Data Quality 

• Stronger governance through Microsoft Purview integration 
• Built-in validation processes ensured clean, reliable data for analytics 

NEXT STEPS 

With the new framework in place, the client is positioned to shift focus from data ingestion to data innovation. Their engineering team is now exploring AI and ML opportunities to further enhance the customer experience. We continue to support the implementation, ensuring ongoing performance and providing knowledge transfer to empower the client’s internal teams. 

Looking to streamline your data pipeline workflows? Let’s talk about how we can help you move faster with confidence. Contact us.