Unlocking the Power of Data: How Data Architectures Are Driving Business Success

In today’s fast paced business environment the need for scalable, real-time and efficient data architectures has become imperative. But many organizations still rely on old data warehouses and batch processing which leads to performance bottlenecks, delayed insights, rigid permission structures and limited cloud integration. These hurdles slow down decision making and restrict business scalability. Recognizing these limitations Digvijay Waghela, a well known data architect and cloud migration expert, helps build scalable high performance data ecosystems.

With an award winning background in big data engineering he has contributed as an Associate editor at SARC. His expertise in cloud native architectures and modern data pipelines has helped organizations eliminate inefficiencies, improve governance and harness real-time analytics. Using technologies like AWS, Snowflake, DBT and Airflow Digvijay helps businesses move from old systems to agile, cost effective and future proof data environments – driving innovation and smarter decision making.

"The future of business is rooted in data. But not any kind of data. It is about agility and scalability to harness real-time insights, something that legacy systems cannot deliver." — says Digvijay.

How Can Businesses Build Scalable and Flexible Data Architectures?

Vertica and other legacy data architectures that were once considered leading-edge technologies cannot handle the advanced data requirements of today's businesses. Legacy systems are unable to provide the necessary speed and flexibility for real-time analytics despite organizations generating 328.77 million terabytes of data daily. Businesses face multiple operational and technical obstacles which prevent growth and innovation. Vertica encounters several major limitations which impact its performance.

The shared resource model in Vertica created bottlenecks during ETL job execution which led to delays in updating Tableau dashboards and generating reports.

Rigid permission structures restricted flexible access management across schemas and tables.

Limited AWS integration reduced operational efficiency and made cloud-based workflows difficult

During the proof-of-concept (POC) phase, Digvijay identified additional issues, including limited data lineage visibility, lack of unit testing on ETLs, poor code modularity, insufficient documentation, and limited scheduling capabilities. The problems affected operational efficiency as well as governance and data reliability.

The DBT Snowflake Migration project started to move the Customer Care team’s data and ETLs from Vertica to Snowflake which resolved the existing limitations. The strategic initiative was created to solve various difficulties the Customer Care team encountered because of Vertica environment constraints. The migration project, which began in June 2024 and successfully concluded in Feb 2025, spanned 10 months and delivered significant improvements in operational efficiency and cloud integration.

“This migration was a game-changer for our operations, unlocking speed, flexibility, and long-term growth,” said Digvijay.

Achieving High-Performance Data Migration From Vertica to Snowflake

Digvijay led the migration of the Customer Care team’s data infrastructure from Vertica to Snowflake. He started by conducting a Proof of Concept (POC) to test Snowflake’s capabilities, identified performance bottlenecks and integration challenges. “Understanding the core challenges is key to designing a solution,” he said. With this knowledge he designed a comprehensive migration plan.

The plan included data transfer methods for 11 TB of compressed data, 172 ETLs and direct vendor API integrations. He worked with cross functional teams to get technical and business requirements aligned and got architectural approval for DBT, Airflow and Snowflake.

Transforming legacy SQL based ETL pipelines into modular, scalable DBT models with improved data lineage and documentation was a key focus. Unit testing was integrated into the models to ensure data reliability. Challenges included redesigning the schema to fit Snowflake’s cloud-native architecture and managing dependencies between legacy and new pipelines to minimize operational disruption. Airflow DAGs were deployed for optimized scheduling, monitoring, and automation.

Key aspects of the migration included:

• Converting legacy ETL pipelines into DBT models with improved scalability and lineage tracking
• Redesigning schemas to accommodate Snowflake’s cloud-native architecture
• Ensuring seamless integration of legacy and new pipelines while avoiding system downtime

Throughout the migration, Digvijay worked hands-on with critical single source of truth (SSOTS), which are integral to the Data Engineering team’s operations. After migration Digvijay optimized ETL and system performance. "A solid data foundation is key to operational efficiency and long term scalability" he said. Continuous monitoring ensured system stability and scalability for future growth.

Transformative Results of the DBT Snowflake Migration Project

The DBT Snowflake Migration Project targeted the multiple issues faced by the Customer Care team that were largely the result of vertica environmental limitations. The data migration from Vertica to Snowflake reduced performance bottlenecks and improved the overall efficiency of the data workflow. The project started in June 2024 and completed in Feb 2025 and delivered measurable results using Snowflake’s scalable and isolated compute resources. Key outcomes are:

• Resolved ETL job and Tableau dashboard delays
• Reduced troubleshooting time by 30% using DBT’s DAG and lineage features
• Identified and resolved 84% of issues on the same day with unit tests and enhanced data quality checks
• Reduced code by 20% with DBT’s modular architecture
• Cut deployment time by 23% by moving from Control-M to Airflow

The migration improved IAM permission structure, simplified user role management and is cost efficient. Snowflake’s seamless integration with AWS enhances data sharing and analytics, fostering collaboration across teams. The introduction of robust data lineage, unit testing, and documentation ensures long-term system reliability. This transformative project positions the organization for a data-driven future, driving operational efficiency, long-term scalability, and continuous optimization.

"Data is the foundation; a robust architecture is what makes it actionable." says Digvijay

With data architects like Digvijay Waghela leading cloud-native migrations, modular data pipelines and automated workflows, companies that adopt these modern data practices are well placed to scale, be more operationally efficient and data driven in today’s environment.