What is Databricks Medallion Architecture? A Deep Dive into Its Why and How

This is an essential inflection point thanks to Medallion Architecture in enterprise data management, which was introduced by Databricks and adopted by Microsoft in their Fabric platform release. This architecture is intended to simplify the problem of structuring and handling data in the context of data lakes and data lake houses. The blog deals with a general overview, beginning with the Data lake &  I will briefly explain what Databricks Medallion Architecture is and how it is related to the key ideas of current data engineering practices. Thus, Medallion Architecture with Bronze, Silver, and Gold layers outlines a plan of systematic data management, processing, and consumption.

It championed gradual evolution, adaptability, and control, which has set the stage for higher-level analytics and other machine learning initiatives.

So, What is medallion architecture, what challenges can it solve, and how should it be done? This blog attempts to answer these questions with some principles that have informed this kind of architecture together with some general implementation advice.

What is Medallion Architecture?

A medallion architecture is also referred to as multi-hop architecture, and this data design pattern is named by Databricks – the premier Data & AI firm. This makes it possible to manage data within a Lakehouse in a very logical, orderly, as well as optimally productive manner. A medallion architecture is a sort of data design blueprint extracted specifically for data management in a lake house context. Your data is categorized into three layers: Bronze Layer, Silver Layer, and Gold Layer.

Subsequently, when the suite of tools was announced in 2023 as Microsoft Data Fabric, the medallion architecture was chosen as the organizing structure of the product. 

In short, medallion architecture is your data design that accommodates data in the context of the data Lake House architecture. Your data must be accepted and go through several levels of validation and conversion before it can be stored for analysis.

 Key Components of the Medallion Architecture

Bronze Layer (Raw Data)

This is the first part of the medallion design known as the medallion architecture. You can take it as the first stage, where raw data in various forms, such as JSON and CSV, are collected and warehoused. Bronze layer data remain unvalidated. Your data remains unaltered throughout the process by retaining its original form. The Data table structures in this layer mirror the source system structure tables in their original form.

They supported additional metadata, including source file name and column to load date/time, process ID, and others. 

This allows the source dataset to be more easily discoverable. Information lineage is kept well. The table in the bronze layer, which stores the data, thus expands in size over a while, and it may contain streaming as well as batch transactions.

Silver Layer (Filtered, Cleansed Data)

This is the second part of the medallion architecture. The raw data from the bronze layer goes through data cleansing, de-duplication and transformation processes of analysis. The silver layer optimizes your data and makes it more available and accessible in such areas as downstream analysis, ad hoc reports, and machine learning. 

This merged and cleansed source data is then fitted to an enterprise view of important business entities, concepts, and transactions. 

Data accuracy is secured with the help of data validation rules that are used in this development. The silver phase could also have defined schemas and more metadata.

Gold Layer (Business-Ready Data)

The gold layer is the last layer of the metal in the medallion architecture. Your data is cleaned, formatted, and extended to make it easily consumable and conform to your specific analytic needs. 

It has been transformed into units that improve the query’s performance. That is denormalized, where the database format is made easier to query and simplified for users. Data in the gold layer should be stored in Delta format to make use of features like the ability to restore a previous version, perhaps in the case of a processing error.

In this way, you have business-ready data for analytics, data science, and machine learning operations. Data here is also combined with other data sources to provide you with more enriching insights in the layer.

Learn how Deletion Vectors enhance data management efficiency, aligning seamlessly with the principles of Databricks Medallion Architecture.

Benefits of Using Medallion Architecture in Databricks 

The integration of medallion architecture in Databricks can be revolutionary for data-driven companies, allowing them to enhance analytics, Ml, and data management.

Here are the standout benefits:

Structured Data Quality and Integrity

Medallion architecture arranges information into three levels of metadata structures. The bronze layer is raw data, the silver layer is processed data, and the gold layer is polished data used for production.

Delta Lake Integration for Reliability and Speed

Delta Lake, one of the key features of Databricks that supports medallion architecture, allows for the performance of ACID transactions and data operations at scale. 

Improved Data Management and Flexibility

The medallion architecture makes it easier for teams to backfill, replicate, and update downstream tables using raw (bronze) data feeds. This flexibility makes it easy to work with transformations and recalculation and minimizes over-dependence on complex ETL procedures and data transformations.

Seamless Integration for Advanced Analytics and Machine Learning

In this way, by focusing on systematic data structuring, medallion architecture benefits Databricks’ ML and analytics workflows by delivering high-quality data that is prepared for modeling. 

Cost Efficiency and Scalability

The tiered approach of medallion architecture, which is in Databricks, means that one can spread resources depending on the stage of data processing. It is useful to implement this approach in order to maximize the use of computing resources and to minimize the costs of storage.

Understanding the Lakehouse Concept

The key idea of the Lakehouse structure is that it stores data in both the data lakes and data warehouses. It allows the structuring and processing of both structured and unstructured data in a single environment and, at the same time, maintains data accuracy and transactional compliance. Delta Lake and other features enabling this functionality are combined in the Databricks Medallion Architecture, which is based on the LakeHouse model.

Role of Delta Lake in Medallion Architecture

Delta Lake is the foundation upon which Databricks has built its Lakehouse data architecture. It provides several key features that enhance Medallion Architecture:

• ACID Transactions: Can guarantee the data processing quality, even in the cases of large numbers of records.
• Time Travel: Allows for querying prior versions of data, allowing for full auditing capabilities as well as the ability to roll back.
• Schema Evolution: Is not affected by changes in data schema because it progresses through the layers of the Medallion Architecture.

Building Data Pipelines with Medallion Architecture

Databricks makes it easy to build data pipelines that incorporate Medallion Architecture. Here’s how you can get started:

Data Ingestion: Initially, raw data will be loaded into the Bronze Layer using the rich set of data sources and APIs provided by Databricks. This is the process of reading or pulling data from any source, such as databases, streaming services, or external files.

Data Processing: Transfer the data to the second layer – the Silver Layer, where activities such as cleaning, merging, and enriching of data take place. They should use Azure Databricks as machine learning and artificial intelligence tools to perform these operations with notebooks and Spark SQL.

Data Aggregation: In the Gold Layer, clean up the data and make it more efficient for the final report and analysis. Feed it to machine learning algorithms, which will also classify the data. Some of Databricks’ optimization features, including Z-Ordering and caching, enhance its query response time and efficiency.

Deployment: Once your pipeline is built, run this pipeline in Databricks Jobs or in the works to process data automatically.

Challenges and Considerations in Implementing Medallion Architecture in Databricks

While Medallion Architecture offers significant benefits, there are several challenges to consider:

Increased Storage Requirements: Medallion Architecture stored data at three levels: Bronze level, which stores raw data; Silver level, which stores clean data; and Gold level, which stores curated data. This approach can increase the size of storage requirements by threefold, and this comes with a cost – especially where there is a large amount of data to be stored.

Complexity and Learning Curve: Being a layered data system, it is crucial to understand that Medallion Architecture must implement the needs of understanding a layered data system. Other teams that are not used to this structure of work will have to learn new ways of working, which consumes a lot of time and energy from data engineers and other technically inclined employees.

Additional Downstream Processing: To meet business needs, further processing is often required in the Gold layer. This increases the complexity of the data pipelines and may add the need for additional tooling for complex transformations.

High Implementation Costs: In addition to storage, the adoption of Medallion Architecture involves costs that include infrastructure, processing, and maintenance that can be very expensive for small organizations or organizations that have very limited capital.

 Medallion Architecture and Data Mesh Alignment

When it comes to Data Mesh, Medallion Architecture complements it and improves decentralized data management and quality. Data Mesh promotes domain-specific ownership, and Medallion Architecture aligns with that ownership by dividing it into three layers: Bronze, Silver, and Gold, which allows data to be organized from ingested to analyzed forms within a certain domain. While Data Mesh prescribes the data-as-a-product approach that would feed data into downstream consuming services, the Gold layer would enhance the utility of the Data Mesh by optimizing the data for analysis.

Both frameworks promote a distributed decision-making structure and inter-domain interoperability while presenting the raw data in a unified form to the system, while letting each domain manage its processed, refined data. It also provides a coherent and scalable data strategy within the domain.

Conclusion 

Medallion Architecture is a structured and effective methodology for categorizing, storing, and refining data in the modern data landscape. Its three-tier architecture—comprising the Bronze, Silver, and Gold layers—enables a progression from raw data ingestion to refined business insights. This structure enhances data quality, availability, and governance, while supporting advanced analytics, machine learning, and data-driven decision-making.

Explore how Autoscaling in Databricks supports the Medallion Architecture by optimizing resources for every data layer.

Implementing Medallion Architecture effectively requires meticulous planning, strong technical infrastructure, and continuous monitoring. Tools like Hevo can support this framework by simplifying data pipelines and integration, enabling organizations to maximize data value, streamline processes, and improve analytic outcomes. Sign up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.

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