Challenge

The bank’s legacy data platform, built on a high-cost, premium-licensed infrastructure, had become a critical bottleneck to growth and operational efficiency.

Built on an end-of-life BDA system, the platform lacked vendor support, scalability, and a viable upgrade path.

Over time, architectural inefficiencies compounded:

• A single schema had grown to over 400,000 tables, driven by poor backup and data design practices
• Query execution times stretched up to 8 minutes, severely impacting analytics and reporting
• Batch ingestion cycles ran long, delaying access to critical business data
• Reporting processes were highly manual, relying on CSV and Excel workflows with no real-time visibility

This resulted in limited agility, delayed insights, and increasing operational risk in a highly regulated banking environment.

Digile implemented a cloud-ready lakehouse architecture built on DigileEdge (powered by Stackable), enabling a complete modernization of the bank’s data platform.

DigileEdge provided a modular, Kubernetes-native data foundation with built-in automation, governance, and reusable pipeline frameworks , accelerating deployment while ensuring scalability and resilience.

The modernized platform simplified data pipelines, reduced metadata complexity, and introduced a standardized integration framework. This shift improved processing speeds at scale, while automated, self-healing features enhanced reliability.

The resulting curated datasets now provide real-time access, accelerating data-driven decision-making while reducing infrastructure and processing costs.

Key transformation outcomes included:

• Simplified data architecture, significantly reducing metadata complexity
• Real-time data ingestion and processing, replacing outdated batch cycles
• Automated, self-healing pipelines, minimizing manual intervention
• Curated, business-ready datasets, enabling faster and more reliable consumption
• Unified governance and observability, ensuring auditability and compliance

Solution Highlights

• Kubernetes-native data platform implementation
• Integration of Change Data Capture (CDC) using Striim
• Real-time streaming and processing enablement
• Centralized data governance and observability framework
• Infrastructure optimization to reduce operational overhead
• Policy-based access control enforced at the platform layer.
• Proactive monitoring with real-time alerts and SLA tracking
• Zero data loss with automated failover and recovery mechanisms
  ‍

Technology Stack

• Stackable
       - Trin
        - NiFi
       - Superset
        - Iceberg
        - Airflow
       - Hive

• DataHub
• Striim

‍

‍