Why Banks Are Moving from Legacy EDW to Modern Lakehouse Architectures

As financial institutions modernize, legacy enterprise data warehouses such as Teradata, Oracle, Netezza, SQL Server, and mainframe-based systems are increasingly challenged by modern scale, cost, and agility requirements. While these platforms have evolved over time, their architectural foundations and licensing models make it difficult to efficiently support real-time analytics, large-scale data science, and AI-driven use cases in a cloud-native world.  

Modern BFSI institutions require cloud-native Lakehouse architectures that reduce the total cost of ownership, support advanced analytics and AI, and meet growing regulatory, security, and operational demands. 

KPI Partners helps BFSI clients transition from aging EDWs to scalable and cost-efficient platforms built on the Databricks Lakehouse, enabling improved agility, governed analytics, and innovation while maintaining regulatory confidence.  

Why Legacy EDWs Are No Longer Enough 

Banks are facing rapidly increasing data volumes driven by digital payments, mobile banking, cybersecurity telemetry, regulatory reporting, and omnichannel customer interactions. Traditional EDWs were primarily designed for structured, batch-oriented reporting workloads, which makes it increasingly difficult to support today’s data diversity and velocity at scale.  

Common pain points include:  

• High licensing and infrastructure costs - Scale-up pricing models and capacity buffers significantly increase TCO.  

• Rigid scaling limitations - Compute and storage are often tightly coupled, limiting workload isolation and elasticity.  

• Long development cycles for new pipelines - Changes to schemas, ingestion, or transformations require extensive coordination and testing.  

• Difficulty integrating semi-structured, unstructured, and streaming data - Event streams, logs, and external data often require parallel platforms outside the EDW.  

• Limited support for end-to-end AI and advanced analytics workflows - While analytics and ML capabilities exist, they are typically fragmented across various tools, which increases operational complexity.  

These challenges directly impact a bank’s ability to respond quickly to regulatory change, deliver timely insights, and operationalize AI at scale.  

Why Databricks Lakehouse Lowers TCO for Banks  

The Databricks Lakehouse architecture combines the data reliability and governance of a warehouse with the scalability and flexibility of a data lake, built on open standards and cloud-native services. This enables BFSI organizations to simplify their data landscape while supporting both traditional analytics and advanced use cases on a single platform.  

Key cost and operational benefits include:  

• Decoupled storage and compute - Independent scaling of storage and multiple compute workloads (BI, regulatory reporting, data science, streaming) enables better cost control and workload isolation.  

• Elastic, consumption-based pricing - Pay-as-you-go computing allows banks to align costs with actual usage, reducing idle capacity during non-peak periods.  

• Platform consolidation - Replace multiple ETL tools, analytical data marts, BI extracts, and shadow systems with a unified Lakehouse powered by Delta Lake.  
  
  
• Optimized performance with Photon - Photon significantly improves price–performance for SQL-heavy workloads, such as finance reporting, regulatory analytics, and interactive BI, thereby reducing overall compute consumption.  

Final Thoughts

By standardizing open data formats and centralized governance, the Databricks Lakehouse enables banks to modernize analytics incrementally while maintaining auditability, security, and compliance.