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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.
102 lines
4.7 KiB
Markdown
102 lines
4.7 KiB
Markdown
# Data Architecture Case Studies
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Real-world architecture transformations. Sources: Hiflylabs, Datalere, Databricks customer stories, Qubika, and Atlan.
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## Commercial Bank: Data Vault 2.0 for Payment Processing
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**Source:** Hiflylabs case study
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**Context:** A commercial bank needed to modernize its payment processing data warehouse without a full system overhaul.
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**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.
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**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.
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**Results:**
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- Core payment data processing: **4-5 minutes → 10 seconds** (30x improvement)
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- Overall process chain: **15-20 min → 8-10 min**
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- Enabled parallel processing of satellite tables
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- Automation toolkit for Data Vault component generation
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- Reverse ETL for historical data migration, legacy systems undisturbed
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**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.
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## Avant: Lakehouse Modernization (Fintech)
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**Source:** Qubika / Databricks
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**Context:** Fintech company needed faster credit decisions, smarter marketing, and automated dispute resolution.
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**Approach:** Migrated to Databricks lakehouse with Delta Lake, MLflow, Unity Catalog. Built end-to-end ML pipelines.
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**Results:**
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- **56% increase** in delivery velocity
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- **60% reduction** in data initiative costs
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- **15+ production ML models**
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- **10% faster** model predictions
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- Lower default rates, improved cash flow
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**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.
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## Janus Henderson: Hybrid Snowflake + Databricks
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**Source:** Datalere (Mark Goodwin, Data Architect at Janus Henderson)
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**Context:** Investment firm with both BI/reporting and data science/streaming needs.
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**Problem:** Adopted both platforms independently, creating duplication and inconsistent data.
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**Approach:** Designed unified architecture with clear ownership boundaries:
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- **Databricks** → complex transformations, data science, streaming
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- **Snowflake** → BI, reporting, governed analytics
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Data flows from Databricks engineering → Snowflake consumption.
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**Results:**
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- Eliminated data duplication
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- Clear ownership per platform
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- BI teams got governed, consistent data
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- Engineering teams kept flexibility
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**Key lesson:** Hybrid works with explicit boundaries and data lifecycle governance. Without those, it's worse than picking one.
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## Insulet: Lakehouse for Medical Manufacturing
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**Source:** Databricks Data + AI Summit
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**Context:** Medical device manufacturer needed to unify Salesforce, SAP, and other data.
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**Approach:** Replaced outdated ETL with Lakeflow, Delta Lake for ACID on the lake.
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**Results:**
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- **12x faster** real-time data processing
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- **83% fewer SQL queries** after replacing ETL
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- **97% lower TCO** by eliminating third-party ETL tools
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**Key lesson:** Biggest win was eliminating expensive middleware entirely, not optimizing it.
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## 7-Eleven: AI at 13,000+ Stores
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**Source:** Databricks Data + AI Summit
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**Context:** Retailer needed AI-driven store insights across a massive footprint.
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**Approach:** Multi-agent marketing assistant on Databricks. RAG for maintenance knowledge retrieval. Unity Catalog for governance at scale.
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**Results:**
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- AI-powered search across all stores
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- Technician productivity improved via RAG
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- Streamlined governance migration
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**Key lesson:** At 13K+ stores, AI isn't optional — it's how you keep per-store costs from growing linearly.
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## Additional References
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- **Delivery Hero** — Data mesh for multi-market scale. Each market = domain. Result: faster onboarding, required significant platform investment.
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- **Intuit** — Data mesh across QuickBooks, TurboTax, Mint. Platform treated as product with its own roadmap.
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- **Dr. Martens** (via Atlan) — Impact analysis from 4-6 weeks to under 30 min via data catalog.
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- **Kiwi.com** (via Atlan) — 53% engineering workload reduction in 90 days.
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## Sources
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- Hiflylabs, "Commercial Bank Data Warehouse Case Study"
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- Datalere, "Using Snowflake and Databricks Together: A Unified Architecture"
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- Databricks, "Data Intelligence in Action: 100+ Data and AI Use Cases"
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- Qubika, "Avant and Qubika" case study
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- Atlan, "Data Mesh: Architecture, Principles, and Case Studies"
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