Innovecture modernized a Fortune 500 firm’s legacy data using Databricks Lakehouse, cutting costs by 70%.
Case Study:
Databricks Lakehouse Modernization
OVERVIEW :
BEFORE :
High costs, data silos, and zero MLOps blocked AI innovation while creating severe compliance risks.
AFTER :
Delivered a secure solution: real-time pipelines, 50+ ML models, and self-service analytics.
AI-Powered Data Modernization for a Fortune 500 Financial Institution — From Legacy Silos to Production AI in 6 Months
A Fortune 500 Financial Services institution engaged Innovecture to undertake a full-scale data modernization — replacing an aging Teradata-based data warehouse and 50+ disconnected data silos with a unified, AI-powered Databricks Lakehouse on Azure. The engagement spanned four delivery phases over six months: platform foundation and data migration, AI-ready pipeline engineering, ML/GenAI deployment, and self-service analytics. The result was a governed, production-grade lakehouse with 50+ ML models in operation, a live RAG-powered compliance assistant, real-time fraud detection, and infrastructure costs reduced by 70%.
CLIENT
Fortune 500 Financial Services Institution
NEED
Legacy Infrastructure & Prohibitive Cost: A Teradata-based warehouse with $4M+ annual infrastructure costs and limited scalability could no longer support the organization’s growing data volumes or analytical demands.
Fragmented Data Across 50+ Silos: Disconnected systems, no centralized data catalog, inconsistent definitions, and manual data movement created reporting conflicts and blocked cross-functional analytics.
Zero AI/ML in Production: All ML models were stuck in experimentation. Data scientists spent 80% of their time on manual data preparation with no MLOps infrastructure, Feature Store, or model management.
Governance & Compliance Exposure: No data lineage, access controls, or audit trails — a critical risk for a regulated financial institution operating under SOC 2, GDPR, and financial regulatory requirements.
SOLUTION
Phase 1 · Lakehouse Foundation & Migration
Provisioned Databricks on Azure with enterprise security and Unity Catalog governance. Migrated 15TB+ from Teradata with zero downtime. Converted 200+ legacy SQL stored procedures to optimized PySpark pipelines using medallion architecture (Bronze → Silver → Gold).
Phase 2 · AI-Ready Data Pipelines
Built 80+ automated Delta Live Tables pipelines with data quality checks. Implemented Auto Loader for real-time ingestion from 50+ sources and Structured Streaming for live transaction monitoring. Created Feature Store with 500+ reusable ML features.
Phase 3 · AI/ML & Generative AI Deployment
Deployed 50+ production models including fraud detection (<50ms inference), credit scoring, churn prediction, and demand forecasting. Built RAG-powered compliance assistant using Databricks Vector Search and fine-tuned an LLM on proprietary financial documents via Mosaic AI.
Phase 4 · Analytics & Self-Service BI
Configured Databricks SQL Warehouses for analyst self-service. Integrated Power BI with Gold layer for executive dashboards. Enabled natural language Text-to-SQL querying for business users and deployed AI-powered dashboards with predictive KPIs and anomaly alerts.
RESULTS
30%
Cost
Reduction
4X
Faster
Reporting
20+
AI Models
in Production
50%
Less Data
Prep Time
• Infrastructure costs cut from $4M to $1.2M/yr by retiring
legacy Teradata warehouse
• Report generation reduced from 24–48 hrs to under 2 hrs;
dashboards now real-time
• From 0 to 50+ production ML models in 6 months —
including fraud detection saving $12M
• Data scientists freed from manual wrangling — now spending
90% on insights and modeling
Additional outcomes included: real-time fraud detection blocking suspicious transactions in under 100ms, 50+ data silos unified into a single governed Lakehouse with full Unity Catalog lineage and audit trails, and a RAG compliance assistant + fine-tuned LLM deployed live on proprietary financial documents — enabling non-technical staff to query regulatory content in natural language.
