Architecting a Medallion Lakehouse

[Khasim_1]

Hi Everyone,

Recently, I led an end-to-end Lakehouse project for a client that was struggling with manual, error-prone reporting and disconnected data silos. Their core business problem was simple yet critical: the business could not trust its own numbers. They needed a modern data platform, but we faced a strict constraint: We could not place any analytical load on their production transactional systems.

In this article, I’ll walk through the three core architectural decisions that allowed us to transform their data landscape into a robust, automated Medallion architecture.

1. The Ingestion Dilemma: Prioritizing Source Stability The client required up-to-date reporting, but querying their production SQL Server databases directly would have impacted their live operations.

• The Decision: We implemented Change Data Capture (CDC) to stream data into our landing zone.
• The Result: By reading transaction logs rather than querying live tables, we achieved near real-time data ingestion with zero performance impact on the source systems. We combined this with Auto Loader for file-based sources, ensuring our ingestion was incremental, schema-aware, and highly resilient.

1. The "Medallion" Strategy: Building for Evolution It’s often tempting to build shortcuts from raw data directly to final reports. However, in a real-world production environment, requirements always change.

• Bronze: Our "Source of Truth" copy. Durable, replayable, and immutable.
• Silver: Where the "real" work happens. We implemented logic to convert raw CDC events into a clean, historical record (SCD Type 2).
• Gold: We deliberately partitioned our Gold layer into separate, business-driven data marts. By decoupling these tables, we ensure that a bug in one department’s logic—such as fulfillment tracking—doesn't impact the accuracy of another department's dashboard, like Finance.

1. The Truth About Referential Integrity in Lakehouses A major point of architectural debate: How do we handle foreign keys? In a high-volume streaming environment, checking referential integrity on every write doesn't scale.

• The Compromise: We declared our foreign key constraints in Unity Catalog to provide metadata for the query optimizer, but we did not enforce them at write-time. Instead, we shifted that check to Data Quality as Code, alerting our team only if orphaned rows appear. This keeps the pipeline performant while maintaining full visibility into data health.

Conclusion The lesson here is that a successful Lakehouse isn't just about the tools—it’s about making the right trade-offs. By prioritizing pipeline reliability through decoupled data marts, managed CDC, and monitored constraints, we didn't just build a pipeline; we built a system that the business can finally trust.