How We Migrated a Fortune 500 Bank to a Modern Data Lakehouse
Client Overview
A leading Fortune 500 bank with over $500 billion in assets under management, operating across 30+ countries with 50,000+ employees. The institution serves millions of retail and institutional customers through its consumer banking, wealth management, and capital markets divisions.
The Challenge
The bank had relied on legacy Oracle and Teradata data warehouses for over 15 years. These systems had become increasingly expensive to maintain, with annual licensing costs exceeding $12 million. As data volumes grew exponentially from digital banking channels, mobile apps, and IoT-enabled branch systems, the legacy infrastructure struggled to keep pace.
Analytical queries that once took minutes were now taking hours, severely impacting the ability of risk analysts, compliance teams, and business intelligence groups to generate timely reports. The bank was consistently missing regulatory reporting deadlines, creating audit risk and potential penalties from multiple regulatory bodies across different jurisdictions.
Data silos had proliferated across business units, with each division maintaining its own extract-transform-load (ETL) pipelines and shadow data stores. This led to inconsistent metrics, duplicated efforts, and a lack of a single source of truth for critical business decisions. Customer data was fragmented across seven different systems, making it impossible to build unified customer profiles for personalization or cross-sell initiatives.
The legacy architecture also lacked the flexibility to support modern data science and machine learning workloads. Data scientists spent 70% of their time on data preparation rather than model development, and deploying models to production required weeks of manual handoffs between data engineering and operations teams.
10x
Faster Queries50%
Cost Reduction99.9%
Data Accuracy3x
Faster ReportingOur Solution
S2 Data Systems designed and implemented a modern data lakehouse architecture on Databricks, unified with Snowflake for structured analytical workloads. The solution was built on a medallion architecture (bronze, silver, gold layers) that provided progressive data quality refinement from raw ingestion through business-ready datasets.
- Medallion Architecture with Delta Lake: We implemented a three-tier data architecture using Delta Lake on cloud object storage. The bronze layer ingests raw data from 40+ source systems with full change data capture. The silver layer applies cleansing, deduplication, and schema standardization. The gold layer delivers business-curated, aggregated datasets optimized for specific reporting and analytics use cases.
- Automated ETL with dbt: All data transformations were codified using dbt (data build tool), providing version-controlled, tested, and documented transformation logic. Over 800 dbt models were created to replace legacy stored procedures and SSIS packages, with built-in data quality tests that run on every pipeline execution.
- Data Governance with Unity Catalog: Databricks Unity Catalog was deployed to provide centralized governance across the entire lakehouse. Fine-grained access controls, automated data lineage tracking, and comprehensive audit logging ensured compliance with SOX, PCI-DSS, and GDPR requirements across all jurisdictions.
- Real-Time Streaming Ingestion: Apache Kafka and Databricks Structured Streaming were integrated to enable near-real-time data ingestion from core banking transaction systems, market data feeds, and digital channel events, reducing data latency from 24 hours to under 5 minutes.
- ML-Ready Feature Store: A centralized feature store was built on the lakehouse, enabling data scientists to discover, reuse, and serve curated features for machine learning models without building custom data pipelines for each project.
The lakehouse architecture has fundamentally transformed how our organization uses data. What used to take our analysts hours now takes seconds, and we finally have a single source of truth that every business unit trusts.
Chief Data Officer, Fortune 500 Bank
Solution Architecture
CDC, Full Load
Quality Tests
Feature Store
Project Timeline
Discovery & Assessment
Cataloged 40+ source systems, 2,000+ tables, and 500+ ETL jobs. Mapped data lineage and identified migration priorities by business impact.
Architecture & Design
Designed the medallion architecture, defined governance policies, and built the foundational infrastructure on Databricks with Terraform IaC.
Phased Migration
Migrated workloads domain-by-domain over 6 months, running dual systems with automated reconciliation to ensure data parity before cutover.
Optimization & Handover
Fine-tuned query performance, trained internal teams on dbt and Databricks, and transitioned to managed support with 24/7 monitoring.
Technology Stack
Frequently Asked Questions
What is a data lakehouse and how does it differ from a traditional data warehouse?
A data lakehouse combines the best of data lakes and data warehouses into a single unified architecture. Unlike traditional data warehouses that require rigid schemas and expensive proprietary storage, a lakehouse stores data in open formats (like Delta Lake or Apache Iceberg) on cost-effective cloud object storage while providing the ACID transactions, schema enforcement, and governance features traditionally found only in data warehouses. This allows organizations to run both BI/reporting and advanced ML workloads on a single copy of the data, eliminating data silos and reducing infrastructure costs.
How long does a typical enterprise data lakehouse migration take?
The timeline depends on the complexity of the existing environment, volume of data, number of downstream consumers, and regulatory requirements. For this Fortune 500 bank, the full migration took approximately 9 months from discovery through production cutover. We used a phased approach, migrating workloads incrementally by business domain, which allowed the bank to realize value from the first sprint while maintaining continuity on legacy systems. Our accelerators and pre-built connectors typically reduce migration timelines by 30-40% compared to building from scratch.
How do you ensure data quality and accuracy during migration?
Data quality is embedded at every stage of our migration methodology. We implement automated data reconciliation frameworks that compare source and target row counts, checksums, and business-rule validations for every migrated table and pipeline. Our medallion architecture (bronze, silver, gold) provides progressive data cleansing, deduplication, and enrichment. Additionally, we deploy data observability tools that continuously monitor for schema drift, freshness anomalies, and distribution changes, alerting the team before data quality issues reach downstream consumers.
What governance and security measures are built into the lakehouse?
We leveraged Databricks Unity Catalog to implement fine-grained access controls at the table, column, and row level, ensuring that sensitive financial data is accessible only to authorized roles. All data is encrypted at rest and in transit, with customer-managed encryption keys. We implemented comprehensive audit logging, data lineage tracking, and automated PII detection and masking. The governance framework supports SOX, PCI-DSS, and GDPR compliance requirements out of the box, with customizable policy engines for institution-specific regulatory mandates.
Can the lakehouse architecture scale to handle future data growth?
Absolutely. The lakehouse architecture is designed for elastic scalability. Compute and storage are fully decoupled, meaning you can scale query processing independently of data storage. The bank can handle 10x data volume growth without any architectural changes, simply by scaling cloud resources on demand. Auto-scaling clusters spin up during peak reporting hours and scale down during off-hours, optimizing cost efficiency. The open data format also ensures zero vendor lock-in, allowing the bank to adopt new compute engines or cloud providers as needs evolve.
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