Optimizing the Aging Data Warehouse of a Century-Old Bank to Deliver 60% Faster, Decision‑Ready Analytics 

About the Client 

Our client, a century-old Indian private sector bank with a strong regional presence and trusted relationships among SMEs and retail customers, wanted to strengthen its digital edge. As digital banking accelerated, the bank needed faster business insights and scalable systems to support future growth. To achieve this, it set out to modernize its core data warehouse and optimize processes for greater efficiency and agility. 

Business Challenge 

Aging data systems slowed reporting and hindered scalability 

The bank’s legacy Greenplum data warehouse was running on an outdated version. The system suffered performance degradation with slow queries, delayed reporting, and high resource consumption, disrupting daily operations. With over 11TB of data growing by 100GB each month, the existing setup lacked the scalability necessary to sustain future business growth and maintain operational continuity, posing significant risks. 

They wanted to thoroughly evaluate their existing data architecture and processes and develop a strategic roadmap for long-term data maturity, operational efficiency, and business agility. 

Zuci’s Consulting Approach and Implementation 

Transforming data infrastructure for scalable, high-performance operations 

Consulting assessment: Setting the direction 

Our consulting team led an eight‑week structured assessment of the bank’s data environment, spanning workload profiles, data ingestion patterns, warehouse performance, and operational governance. The diagnostic phase uncovered key pain points such as unbalanced resource utilization, redundant data processing, and fragile ETL pipelines, all of which constrained performance and agility. 

 
From this, we delivered a clear sequence of strategic initiatives: optimize the bank’s Greenplum database, strengthen data resilience, and redesign its ETL ecosystem for efficiency and scalability. 

Optimized Greenplum database for high-volume operations 

Our specialists designed a comprehensive optimization plan targeting database performance and resource efficiency. Using automated query profiling, schema analysis, we eliminated performance bottlenecks and modernized infrastructure components. 

Key actions included: 

• Conducting deep workload and query analysis for faster transaction reporting and improved responsiveness for business dashboards 

• Upgrading Greenplum components and configurations to handle surging data volumes, ensuring the warehouse could support growing lending needs without performance drops 

• Fine‑tuning CPU, memory, and storage to handle peak workloads efficiently, while cutting infrastructure costs 

• Automating daily health‑check routines like cache clearing, dead‑tuple cleanup, and index maintenance to maintain consistent system performance with minimal DBA overhead 

• Strengthening disaster‑recovery to ensure uninterrupted banking operations and reporting in high-load conditions 

Redefined ETL processes for scalable data operations 

We replaced legacy shellscript‑based ETL pipelines with automated orchestration through Apache Airflow. Jobs were rewritten as Python DAGs to handle complex data inputs, adding real‑time monitoring and alerts. This reduced manual intervention, halved job runtimes, and ensured timely data availability for analytics and business reporting.