What Is Medallion Architecture?

Organizations store massive amounts of data into their storage systems. In its raw form, this data isn’t useful. It needs to be refined, structured and processed to yield valuable insights. This means cleaning the raw data for accuracy. You’ll remove duplicates and fix any missing values in the customer database. Structuring refers to organizing data into a logical and standardized format. For example, categorizing sales data by region and product. Processing prepares data for analysis, for example, aggregating daily sales into monthly reports. Here’s the catch: as organizations collect more data, it gets harder to manage, process and trust. That’s why the medallion architecture was created. It provides a clear, structured way to refine, organize and process data.

The medallion architecture isn’t tied to any specific modeling method. It’s a data design pattern that uses a three-layered approach: Bronze, Silver, and Gold. This method helps manage and refine data. Organizations adopt this architecture to enhance data quality, improve governance and maintain flexibility. This lays a strong foundation for advanced analytics and AI initiatives.

What Are the Challenges of a Data Lake?

The first challenge is managing everything in a data lake. This includes customer interactions, sensor readings, financial transactions and social media feeds. It’s flexible, scalable and can store data in any format, but lacks structure. Without structure, this flood of information becomes a data swamp. It’s filled with duplicate records, conflicting formats and outdated files.

Organizations use ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) to get raw data ready for analysis. ETL means defining the target structure first. Then, transformations can happen. Data is processed before it is loaded into a system. On the other hand, ELT loads raw data first and applies transformations later. This approach gives more flexibility. It works well in cloud settings and data lakes that store lots of unprocessed data. In both cases, we use several steps to prepare the data for analysis. These steps include cleansing, aggregation and enrichment.

If each step is stored as an intermediate dataset, the number can quickly grow and become hard to manage. Many of these datasets will be used just once or twice. After that, they become outdated but still take up space. No one wants to delete this data. They think it might still help some team. So, enterprises end up with lots of outdated, redundant and irrelevant information.

Second is the variety problem — structured, semi-structured and unstructured. Not all data from different systems fits neatly into databases. Some data arrives as messy text files, images, videos or even form survey responses. Different formats need different processing methods. For text, we use natural language processing (NLP). For images and videos, computer vision is key. Statistical parsing works for survey responses. Without these processing techniques, data lakes might get fragmented. Different teams may use various tools to handle their own data subsets.

Then there is the speed at which data is streaming in. Today, businesses must process data in real time. Data lakes use a schema-on-read method. This means data is stored in its raw form and is structured only when it’s queried. Transformations that happen on the fly slow down analysis and use a lot of computing power. Real-time querying becomes inefficient, making it difficult to react swiftly to market changes.

Lastly, there are trust issues. Data comes from many sources—CRM systems, IoT devices, vendors, and social media. This makes it tough to know what’s accurate. Teams always question its authenticity. If teams can’t trust the data, problems arise. When the same metric appears differently across teams, trust falls apart.

How Did Medallion Architecture in Lakehouse Solve Data Lake Challenges

Lakehouse combines data lakes and data warehouses. It offers structure, governance and performance improvements for large data sets. It uses a schema-on-write model. This means data is structured and validated before storage.

Lakehouse handles both structured and unstructured data within one architecture. It uses open table formats like Delta Lake, Apache Iceberg and Hudi to keep everything organized and efficient. The platform combines various processing frameworks. This lets all departments handle their data subsets in one ecosystem.

Unstructured data is indexed and discoverable via metadata layers. It also supports ACID transactions—atomicity, consistency, isolation and durability. This ensures reliable data updates. Also, a lakehouse doesn’t keep full copies of transformed data. It only tracks and updates the changes made.

This way, a lakehouse solves most of the challenges presented by data lakes. However, it does not inherently organize data for quality assurance or incremental refinement. It offers schema enforcement and governance. Yet, it can’t guarantee that the ingested data is clean, accurate and ready for analysis. Querying raw or semi-structured data directly in a lakehouse can be slow and inefficient.

Organizations are now using medallion architecture for a more structured way to implement and optimize a lakehouse. It organizes data step by step to boost its quality. Data passes through three layers before reaching analysts for decision-making.

What Are the Three Layers of Medallion Architecture?

In medallion architecture, or multi-hop architecture, each layer has a specific role in data processing. The three layers are called bronze, silver and gold.

Bronze layer

The first layer of medallion architecture is the raw data reservoir. All incoming data from outside sources—structured, semi-structured, or unstructured—gets stored as is. This layer does not apply any business rules, aggregations, or transformations to change its content. It also keeps metadata about the raw data. This includes load timestamps, process IDs, and change data capture (CDC).

Load timestamps to show when data entered the bronze layer. Also, track process IDs to identify which pipeline or job handled the data. The CDC tracks changes to source data. It keeps historical archives to show how data has changed over time. This metadata appending helps organizations keep track of their data. It supports lineage audits and allows for easy reprocessing when necessary.

This layer acts as a foundational stage to lay the groundwork for a robust data pipeline. It makes sure businesses can always access the original dataset. This access lets them roll back changes, analyze trends over time, or prepare data for further processing in the silver layer.

Silver layer

Raw data from the bronze layer lands here. It is messy, inconsistent and full of duplicates. Think of this layer as a cleanup crew that follows the ELT approach with minimal steps. Heavy lifting, like aggregations, complex joins, and deep calculations, is not part of this layer. It involves the following tasks: data cleansing, verification, conforming, matching and integration.

It starts by cleaning up the junk. This includes fixing typos, removing duplicates, standardizing formats and handling missing values. The second step is data verification. This ensures the data matches quality standards by validating its schema and format. Also, if the data is consistent across sources and complies with governance rules. The third step is data conforming. This aligns data from various systems into a common structure. It also applies business rules to ensure the data meets company-wide standards. In the fourth step, relationships between different datasets are identified. This helps link related records and create a universal primary key for easier consolidation.

The data from the silver layer in medallion architecture is well-structured and trustworthy. It is ready for self-service analytics, reporting, machine learning or advanced modeling.

Gold layer

This layer focuses on providing clean, curated datasets. These datasets are designed for specific analytical and reporting needs. It stores data in a denormalized, read-optimized format to reduce the need for complex joins and enhance query performance. The data is optimized by using aggregation and enrichment methods that involve summarizing it to the required granularity. It serves as the end goal of the entire data pipeline, helping businesses get the insights they need to drive better decisions.

This is how these three layers of medallion architecture allow organizations to extract value from their data at every stage. Now, let’s explore how medallion architecture benefits businesses.

What are the Benefits of Medallion Architecture

Medallion architecture presents several benefits, like making sure data is clean, reliable, and easy to use. Here are some benefits:

• Clean and reliable data: Data goes through multiple stages of refinement—from raw to structured to fully optimized. The tiered structure reduces the risk of errors.

• Scalability and flexibility: New data gets processed incrementally. Since it supports parallel data pipelines, different layers can work at the same time without slowing things down. Each layer is optimized individually to align with business needs. Also, this architecture is flexible as it supports disparate data types and sources.

• Visibility and control: Each transformation follows a clear structure. This makes it easy to track data sources for audits, fix problems, or ensure compliance.

• Speed and performance: The gold layer of medallion architecture stores data in a denormalized format, so there are not many complex joins in queries. This enables reports to load quickly and dashboard to be responsive improving query performance.

• Historical data preservation: Bronze layer in this architecture acts like a safety net because it contains all the data in its raw form. So, enterprises have the option to roll back changes.

Medallion architecture creates a seamless pipeline that ensures better quality, scalability and reliability at every stage. It makes sure the data is ready for important tasks. This includes building reports, training ML models and making strategic decisions to meet business goals.