Enterprise Customer 360 Data Platform Implementation using Natural Keys, Row Hashes & Medallion Architecture
Group: Business Requirement
|Product Category: Cloud & Data Engineering
|Sub Category: Data Modeling & Dimensional Modeling
About this Product
Enterprise Customer 360 Data Platform Implementation using Natural Keys, Row Hashes & Medallion Architecture is a practical implementation guide that teaches you how to build a production-ready Customer 360 data platform using modern ETL and Lakehouse design principles.
This guide demonstrates how to unify customer data from multiple CRM systems into a single Customer 360 model without relying on source primary keys or update timestamps. You'll implement a Bronze–Silver–Gold architecture with full snapshot ingestion, natural-key and row-hash change detection, entity resolution, survivorship rules, soft deletes, audit lineage, and production-ready data quality practices.
Product Highlights
- Build a reusable Customer 360 ETL framework using a Medallion architecture.
- Implement Natural Key and Row Hash based change detection.
- Consolidate customer records across multiple CRM systems.
- Apply entity resolution, survivorship, and soft-delete logic.
- Implement audit metadata, reconciliation, and data quality validation.
- Learn scalable and production-ready Customer 360 engineering practices.
By completing this guide, you will:
- Build enterprise-ready Customer 360 ETL pipelines.
- Implement full snapshot ingestion with incremental change detection.
- Apply entity matching and golden record creation techniques.
- Design resilient Bronze, Silver, and Gold data layers.
- Develop reusable Customer 360 frameworks for multi-source integration.
Why this project matters
Customer data is often fragmented across multiple business systems, making unified reporting and analytics difficult. This guide teaches industry-standard techniques for building scalable Customer 360 platforms that create trusted golden records, preserve data lineage, and enable reliable cross-system analytics—skills expected in modern Data Engineering roles.
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