Medallion architecture

For an optimal experience, provide your email below and one of our lifeguards will send you a link to start swimming in the lake! The Medallion Architecture is a software design pattern that organizes a data pipeline into three distinct tiers based medallion architecture functionality: bronze, silver, and gold, medallion architecture. The bronze tier represents the core functionality of the system, while the silver and gold tiers build on top of the previous tier, offering more advanced features.

As the amount of data produced increases and the technologies required to process it grow, organisations are looking to advanced data architectures to meet new needs. In this context, the Medallion architecture emerges, a novel perspective that fits perfectly with the data lakehouse approach and promises to promote data quality. The amount of data continues to grow every year. According to the latest statistics from Forbes , experts anticipate that the total volume of data worldwide will increase from The exponential increase in the amount of data generated is putting the focus on disciplines such as data governance and data quality.

Medallion architecture

A medallion architecture is a data design pattern, coined by Databricks, used to logically organize data in a lakehouse, with the goal of incrementally improving the quality of data as it flows through various layers. This architecture consists of three distinct layers — bronze raw , silver validated and gold enriched — each representing progressively higher levels of quality. Medallion architectures are sometimes referred to as "multi-hop" architectures. Data is saved without processing or transformation. This might be saving logs from an application to a distributed file system or streaming events from Kafka. Note that the transformations here should be light modifications, not aggregations or enrichments. From our first example, those logs might be parsed slightly to extract useful information— like unnesting structs or eliminating abbreviations. Our events might be standardized to coalesce naming conventions or split a single stream into multiple tables. After the gold stage, data should be ready for consumption by downstream teams, like analytics, data science, or ML ops. The final stage gold used for analytics is entirely separate than the raw stage bronze used for ingestion. Medallion architecture provides a framework for data cleaning, not data architecture. For that reason, it might not be practical for data teams with intensive storage demands.

This can help reduce downtime and improve overall system performance.

Therefore, we need to examine how to design the data model for the lakehouse architecture. The most common pattern for modeling the data in the lakehouse is called a medallion. But, why medallion? The same as for the lakehouse concept, credits for being pioneers in the medallion approach goes to Databricks. Simply said, medallion architecture assumes that your data within the lakehouse will be organized in three different layers: bronze, silver, and gold. Now, you may also hear terms such as: Raw, Validated, Enriched, which I personally prefer. Or, Raw, Validated, Curated…But, essentially, the idea is the same — to have different layers of data in the lakehouse, that are of different quality and serve different purposes.

The medallion architecture is a design pattern for data lakehouses that helps organizations effectively manage and analyze data at scale. This approach addresses the challenges of data processing, storage, and retrieval by organizing data into different layers based on its processing and access requirements. Below we have a high level look at the medallion architecture, discuss some benefits, explain when you may consider using it, and share some best practices for implementing it in your data lakehouse. The medallion architecture divides data in a data lakehouse into three primary layers, each serving a specific purpose:. Bronze Layer: Also known as the raw or ingestion layer, this layer stores raw, unprocessed data ingested from various sources in its native format. The data in the Bronze layer is typically immutable and retained for compliance and historical purposes. Silver Layer: This layer contains processed, cleaned, and enriched data derived from the Bronze layer.

Medallion architecture

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This can help reduce downtime and improve overall system performance. This architecture enables flexible data management, adapting to changing market demands and providing a single source of truth in an organisation. As such, a medallion architecture is not a drop-in replacement for existing data transformation solutions. In the example above, we are dynamically building the path to the file we are committing as the target. Companies are trying to solve this puzzle with flexible data architectures that allow them to adopt new technologies and approaches to data management as needs arise , which is essential to keep up with a changing environment. In regards to storage format, the bronze layer usually stores the data in one of the efficient columnar formats we examined in the articles — parquet or delta format. This approach facilitates rollbacks and lineage, and allows you to promote data atomically when you have multistep steps within a transformation i. Copy Code Copied Use a different Browser. In short, in a Medallion architecture, the quality and structure of data improves as it passes through each layer. Contact Us. Durability ensures that transactions are not complete until they are recorded in memory. The purpose of the bronze layer is to serve as a repository of the historical archive of source data, and enable quick data reprocessing when necessary, without the need to connect to myriad external source systems again. Some people believe it is better to have a more descriptive name of what the layers are. Columar storage is great because it stores data in columns rather than rows.

Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. The medallion architecture describes a series of data layers that denote the quality of data stored in the lakehouse. Databricks recommends taking a multi-layered approach to building a single source of truth for enterprise data products.

Usually in the bronze layer data will be loaded incrementally and will grow over time. Other names Different implementations will often use different names for the layers and there are no rules about what names must be used. Award Winning Databricks Partner. Data in the bronze layer should be stored in a columnar format such as Parquet or Delta. Data can be imported into the bronze data repository by creating an ingestion branch, uploading the data to the ingestion branch, committing the change, and then merging the ingestion branch into the main branch. In terms of storage, similar to the previous two layers, data is stored in an efficient format, preferably Delta, or alternatively Parquet. Delta Lake is a storage format based on Apache Parquet. A table can be created that sums customer orders by year to answer this question. Data in the silver layer should ideally be stored in Delta format to start to take advantage of the features of Delta. From our first example, those logs might be parsed slightly to extract useful information— like unnesting structs or eliminating abbreviations. Medallion Architecture: What is it? Privacy Overview This website uses cookies to improve your experience. Some teams might prefer those processes remain separate, rather than having analysts develop in the gold layer. In this context, the Medallion architecture emerges, a novel perspective that fits perfectly with the data lakehouse approach and promises to promote data quality.