Implement medallion lakehouse architecture in Microsoft Fabric

This article introduces medallion lake architecture and describes how you can implement a lakehouse in Microsoft Fabric. It's targeted at multiple audiences:

  • Data engineers: Technical staff who design, build, and maintain infrastructures and systems that enable their organization to collect, store, process, and analyze large volumes of data.
  • Center of Excellence, IT, and BI team: The teams that are responsible for overseeing analytics throughout the organization.
  • Fabric administrators: The administrators who are responsible for overseeing Fabric in the organization.

The medallion lakehouse architecture, commonly known as medallion architecture, is a design pattern that's used by organizations to logically organize data in a lakehouse. It's the recommended design approach for Fabric.

Medallion architecture comprises three distinct layers—or zones. Each layer indicates the quality of data stored in the lakehouse, with higher levels representing higher quality. This multi-layered approach helps you to build a single source of truth for enterprise data products.

Importantly, medallion architecture guarantees the ACID set of properties (Atomicity, Consistency, Isolation, and Durability) as data progresses through the layers. Starting with raw data, a series of validations and transformations prepares data that's optimized for efficient analytics. There are three medallion stages: bronze (raw), silver (validated), and gold (enriched).

For more information, see What is the medallion lakehouse architecture?.

OneLake and lakehouse in Fabric

The basis of a modern data warehouse is a data lake. Microsoft OneLake, which is a single, unified, logical data lake for your entire organization. It comes automatically provisioned with every Fabric tenant, and it's designed to be the single location for all your analytics data.

You can use OneLake to:

  • Remove silos and reduce management effort. All organizational data is stored, managed, and secured within one data lake resource. Because OneLake is provisioned with your Fabric tenant, there are no more resources to provision or manage.
  • Reduce data movement and duplication. The objective of OneLake is to store only one copy of data. Fewer copies of data results in fewer data movement processes, and that leads to efficiency gains and reduction in complexity. If necessary, you can create a shortcut to reference data stored in other locations, rather than copying it to OneLake.
  • Use with multiple analytical engines. The data in OneLake is stored in an open format. That way, the data can be queried by various analytical engines, including Analysis Services (used by Power BI), T-SQL, and Spark. Other non-Fabric applications can use APIs and SDKs to access OneLake as well.

For more information, see OneLake, the OneDrive for data.

To store data in OneLake, you create a lakehouse in Fabric. A lakehouse is a data architecture platform for storing, managing, and analyzing structured and unstructured data in a single location. It can easily scale to large data volumes of all file types and sizes, and because it's stored in a single location, it's easily shared and reused across the organization.

Each lakehouse has a built-in SQL analytics endpoint that unlocks data warehouse capabilities without the need to move data. That means you can query your data in the lakehouse by using SQL queries and without any special setup.

For more information, see What is a lakehouse in Microsoft Fabric?.

Tables and files

When you create a lakehouse in Fabric, two physical storage locations are provisioned automatically for tables and files.

  • Tables is a managed area for hosting tables of all formats in Spark (CSV, Parquet, or Delta). All tables, whether automatically or explicitly created, are recognized as tables in the lakehouse. Also, any Delta tables, which are Parquet data files with a file-based transaction log, are recognized as tables as well.
  • Files is an unmanaged area for storing data in any file format. Any Delta files stored in this area aren't automatically recognized as tables. If you want to create a table over a Delta Lake folder in the unmanaged area, you'll need to explicitly create a shortcut or an external table with a location that points to the unmanaged folder that contains the Delta Lake files in Spark.

The main distinction between the managed area (tables) and the unmanaged area (files) is the automatic table discovery and registration process. This process runs over any folder created in the managed area only, but not in the unmanaged area.

In Microsoft Fabric, the Lakehouse explorer provides a unified graphical representation of the whole Lakehouse for users to navigate, access, and update their data.

For more information about automatic table discovery, see Automatic table discovery and registration.

Delta Lake storage

Delta Lake is an optimized storage layer that provides the foundation for storing data and tables. It supports ACID transactions for big data workloads, and for this reason it's the default storage format in a Fabric lakehouse.

Importantly, Delta Lake delivers reliability, security, and performance in the lakehouse for both streaming and batch operations. Internally, it stores data in Parquet file format, however, it also maintains transaction logs and statistics that provide features and performance improvement over the standard Parquet format.

Delta Lake format over generic file formats delivers the following main benefits.

  • Support for ACID properties, and especially durability to prevent data corruption.
  • Faster read queries.
  • Increased data freshness.
  • Support for both batch and streaming workloads.
  • Support for data rollback by using Delta Lake time travel.
  • Enhanced regulatory compliance and audit by using the Delta Lake table history.

Fabric standardizes storage file format with Delta Lake, and by default, every workload engine in Fabric creates Delta tables when you write data to a new table. For more information, see Lakehouse and Delta Lake tables.

Medallion architecture in Fabric

The goal of medallion architecture is to incrementally and progressively improve the structure and quality of data as it progresses through each stage.

Medallion architecture consists of three distinct layers (or zones).

  • Bronze: Also known as the raw zone, this first layer stores source data in its original format. The data in this layer is typically append-only and immutable.
  • Silver: Also known as the enriched zone, this layer stores data sourced from the bronze layer. The raw data has been cleansed and standardized, and it's now structured as tables (rows and columns). It might also be integrated with other data to provide an enterprise view of all business entities, like customer, product, and others.
  • Gold: Also known as the curated zone, this final layer stores data sourced from the silver layer. The data is refined to meet specific downstream business and analytics requirements. Tables typically conform to star schema design, which supports the development of data models that are optimized for performance and usability.


Because a Fabric lakehouse represents a single zone, you create one lakehouse for each of the three zones.

Diagram of an example of OneLake medallion architecture that shows data sources, prepare and transform with bronze, silver, and gold layers, and analyzing with the SQL analytics endpoint and Power BI.

In a typical medallion architecture implementation in Fabric, the bronze zone stores the data in the same format as the data source. When the data source is a relational database, Delta tables are a good choice. The silver and gold zones contain Delta tables.


To learn how to create a lakehouse, work through the Lakehouse end-to-end scenario tutorial.

Fabric lakehouse guidance

This section provides you with guidance related to implementing your Fabric lakehouse using medallion architecture.

Deployment model

To implement medallion architecture in Fabric, you can either use lakehouses (one for each zone), a data warehouse, or combination of both. Your decision should be based on your preference and the expertise of your team. Keep in mind that Fabric provides you with flexibility: You can use different analytic engines that work on the one copy of your data in OneLake.

Here are two patterns to consider.

  • Pattern 1: Create each zone as a lakehouse. In this case, business users access data by using the SQL analytics endpoint.
  • Pattern 2: Create the bronze and silver zones as lakehouses, and the gold zone as data warehouse. In this case, business users access data by using the data warehouse endpoint.

While you can create all lakehouses in a single Fabric workspace, we recommend that you create each lakehouse in its own, separate Fabric workspace. This approach provides you with more control and better governance at the zone level.

For the bronze zone, we recommend that you store the data in its original format, or use Parquet or Delta Lake. Whenever possible, keep the data in its original format. If the source data is from OneLake, Azure Data Lake Store Gen2 (ADLS Gen2), Amazon S3, or Google, create a shortcut in the bronze zone instead of copying the data across.

For the silver and gold zones, we recommend that you use Delta tables because of the extra capabilities and performance enhancements they provide. Fabric standardizes on Delta Lake format, and by default every engine in Fabric writes data in this format. Further, these engines use V-Order write-time optimization to the Parquet file format. That optimization enables extremely fast reads by Fabric compute engines, such as Power BI, SQL, Spark, and others. For more information, see Delta Lake table optimization and V-Order.

Lastly, today many organizations face massive growth in data volumes, together with an increasing need to organize and manage that data in a logical way while facilitating more targeted and efficient use and governance. That can lead you to establish and manage a decentralized or federated data organization with governance.

To meet this objective, consider implementing a data mesh architecture. Data mesh is an architectural pattern that focuses on creating data domains that offer data as a product.

You can create a data mesh architecture for your data estate in Fabric by creating data domains. You might create domains that map to your business domains, for example, marketing, sales, inventory, human resources, and others. You can then implement medallion architecture by setting up data zones within each of your domains.

For more information about domains, see Domains.

Understand Delta table data storage

This section describes other guidance topics related to implementing a medallion lakehouse architecture in Fabric.

File size

Generally, a big data platform performs better when it has a small number of large files rather than a large number of small files. That's because performance degradation occurs when the compute engine must manage many metadata and file operations. For better query performance, we recommend that you aim for data files that are approximately 1 GB in size.

Delta Lake has a feature called predictive optimization. Predictive optimization removes the need to manually manage maintenance operations for Delta tables. When this feature is enabled, Delta Lake automatically identifies tables that would benefit from maintenance operations, and then optimizes their storage. It can transparently coalesce many smaller files into large files, and without any impact on other readers and writers of the data. While this feature should form part of your operational excellence and your data preparation work, Fabric has the capability to optimize these data files during data write too. For more information, see Predictive optimization for Delta Lake.

Historical retention

By default, Delta Lake maintains a history of all changes made That means the size of historical metadata grows over time. Based on your business requirements, you should aim to keep historical data only for a certain period of time to reduce your storage costs. Consider retaining historical data for only the last month, or other appropriate period of time.

You can remove older historical data from a Delta table by using the VACUUM command. However, be aware that by default you can't delete historical data within the last seven days—that's to maintain the consistency in data. The default number of days is controlled by the table property delta.deletedFileRetentionDuration = "interval <interval>". It determines the period of time that a file must be deleted before it can be considered a candidate for a vacuum operation.

Table partitions

When you store data in each zone, we recommended that you use a partitioned folder structure wherever applicable. This technique helps to improve data manageability and query performance. Generally, partitioned data in a folder structure results in faster search for specific data entries thanks to partition pruning/elimination.

Typically, you append data to your target table as new data arrives. However, in some cases you might merge data because you need to update existing data at the same time. In that case, you can perform an upsert operation by using the MERGE command. When your target table is partitioned, be sure to use a partition filter to speed up the operation. That way, the engine can eliminate partitions that don't require updating.

Data access

Lastly, you should plan and control who needs access to specific data in the lakehouse. You should also understand the various transaction patterns they're going to use while accessing this data. You can then define the right table partitioning scheme, and data collocation with Delta Lake Z-order indexes.

For more information about implementing a Fabric lakehouse, see the following resources.