Magnus Hedemark
15ba8bb82a
A virtual data architect for teams without one. Includes: - 8 reference files covering architecture patterns, cloud platforms, governance maturity, anti-patterns, compliance, vendor evaluation, case studies, and discovery frameworks - Interactive governance maturity assessment script - ADR template for capturing architecture decisions - 4 Mermaid decision trees for common architecture choices - QuickScan and proactive discovery flow for users who don't know where to start No personal or identifying information included.
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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"