# Data Architecture Case Studies

Real-world architecture transformations. Sources: Hiflylabs, Datalere, Databricks customer stories, Qubika, and Atlan.

## Commercial Bank: Data Vault 2.0 for Payment Processing

**Source:** Hiflylabs case study
**Context:** A commercial bank needed to modernize its payment processing data warehouse without a full system overhaul.

**Problem:** Legacy payment processing architecture was slow and inflexible. Core payment data processing took 4-5 minutes per run, with the full process chain taking 15-20 minutes. The legacy system couldn't support the bank's evolving business rules.

**Approach:** Rather than rebuilding everything, narrowed focus to two key data sources and three downstream processes. Hybrid approach — bridging the legacy system with Data Vault 2.0 methodology through 15 views and a new hub-link-satellite model on Snowflake.

**Results:**
- Core payment data processing: **4-5 minutes → 10 seconds** (30x improvement)
- Overall process chain: **15-20 min → 8-10 min**
- Enabled parallel processing of satellite tables
- Automation toolkit for Data Vault component generation
- Reverse ETL for historical data migration, legacy systems undisturbed

**Key lesson:** Data Vault doesn't require a big bang. Targeted application to the highest-pain area delivered in two sprints (one month). Automation toolkit meant they could extend to other domains.

## Avant: Lakehouse Modernization (Fintech)

**Source:** Qubika / Databricks
**Context:** Fintech company needed faster credit decisions, smarter marketing, and automated dispute resolution.

**Approach:** Migrated to Databricks lakehouse with Delta Lake, MLflow, Unity Catalog. Built end-to-end ML pipelines.

**Results:**
- **56% increase** in delivery velocity
- **60% reduction** in data initiative costs
- **15+ production ML models**
- **10% faster** model predictions
- Lower default rates, improved cash flow

**Key lesson:** Lakehouse served both analytics and ML from the same platform, eliminating data duplication. Cost reduction came from retiring the legacy stack, not optimizing it.

## Janus Henderson: Hybrid Snowflake + Databricks

**Source:** Datalere (Mark Goodwin, Data Architect at Janus Henderson)
**Context:** Investment firm with both BI/reporting and data science/streaming needs.

**Problem:** Adopted both platforms independently, creating duplication and inconsistent data.

**Approach:** Designed unified architecture with clear ownership boundaries:
- **Databricks** → complex transformations, data science, streaming
- **Snowflake** → BI, reporting, governed analytics

Data flows from Databricks engineering → Snowflake consumption.

**Results:**
- Eliminated data duplication
- Clear ownership per platform
- BI teams got governed, consistent data
- Engineering teams kept flexibility

**Key lesson:** Hybrid works with explicit boundaries and data lifecycle governance. Without those, it's worse than picking one.

## Insulet: Lakehouse for Medical Manufacturing

**Source:** Databricks Data + AI Summit
**Context:** Medical device manufacturer needed to unify Salesforce, SAP, and other data.

**Approach:** Replaced outdated ETL with Lakeflow, Delta Lake for ACID on the lake.

**Results:**
- **12x faster** real-time data processing
- **83% fewer SQL queries** after replacing ETL
- **97% lower TCO** by eliminating third-party ETL tools

**Key lesson:** Biggest win was eliminating expensive middleware entirely, not optimizing it.

## 7-Eleven: AI at 13,000+ Stores

**Source:** Databricks Data + AI Summit
**Context:** Retailer needed AI-driven store insights across a massive footprint.

**Approach:** Multi-agent marketing assistant on Databricks. RAG for maintenance knowledge retrieval. Unity Catalog for governance at scale.

**Results:**
- AI-powered search across all stores
- Technician productivity improved via RAG
- Streamlined governance migration

**Key lesson:** At 13K+ stores, AI isn't optional — it's how you keep per-store costs from growing linearly.

## Additional References

- **Delivery Hero** — Data mesh for multi-market scale. Each market = domain. Result: faster onboarding, required significant platform investment.
- **Intuit** — Data mesh across QuickBooks, TurboTax, Mint. Platform treated as product with its own roadmap.
- **Dr. Martens** (via Atlan) — Impact analysis from 4-6 weeks to under 30 min via data catalog.
- **Kiwi.com** (via Atlan) — 53% engineering workload reduction in 90 days.

## Sources

- Hiflylabs, "Commercial Bank Data Warehouse Case Study"
- Datalere, "Using Snowflake and Databricks Together: A Unified Architecture"
- Databricks, "Data Intelligence in Action: 100+ Data and AI Use Cases"
- Qubika, "Avant and Qubika" case study
- Atlan, "Data Mesh: Architecture, Principles, and Case Studies"