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title: Building the Medallion Architecture with Delta Lake
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# Building the Medallion Architecture with Delta Lake

**[Delta Lake Blog](https://daily.dev/sources/delta-lake)** · 7 min read · 0 upvotes · 0 comments

## Summary

An explanation of how to build ETL pipelines using the Medallion architecture (bronze, silver, gold table tiers) with Delta Lake as the underlying table format. Walks through an example e-commerce BI pipeline using Kafka, Flink, and Spark to ingest, clean, and aggregate transaction data, then highlights Delta Lake features that help—ACID transactions, Change Data Feed, deletion vectors, liquid clustering, and multi-engine support. Also covers organizing tables with Unity Catalog's namespace and discusses when teams should deviate from the strict Medallion model in favor of flexible, business-driven data architecture.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://delta.io/blog/delta-lake-medallion-architecture>

## Questions this post answers

### What are the bronze, silver, and gold layers in the Medallion architecture?

Bronze tables hold raw ingested data, silver tables hold cleaned and joined datasets such as deduplicated transactions joined with customer data, and gold tables hold business-level aggregates like customer-level HyperLogLog sketches used for fast BI dashboards. Each tier progressively refines data for downstream consumption by data engineers, scientists, and analysts.

_Teams designing tiered data pipelines can track architecture patterns like this via daily.dev._

### Why use Delta Lake instead of a plain data lake for a Medallion architecture pipeline?

Delta Lake supports ACID transactions, giving consistency, atomicity, and isolation that plain data lakes lack, which prevents pipeline corruption during frequent small writes such as per-minute Kafka ingestion. It also offers Change Data Feed for incremental updates, deletion vectors for faster delete/update/merge operations, and liquid clustering for faster downstream queries.

_Evaluating table formats for ETL reliability is easier when developers compare notes on daily.dev._

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---

Tags: [#data-engineering](https://daily.dev/tags/data-engineering), [#etl](https://daily.dev/tags/etl), [#unity-catalog](https://daily.dev/tags/unity-catalog)

[View this post on daily.dev](https://daily.dev/posts/building-the-medallion-architecture-with-delta-lake-et1bhbzd6)

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