From Raw to Gold in 48 Hours: Building a Modern Medallion Architecture

Today, many organizations face challenges with fragmented systems, diverse data formats, and slow-growing pipelines. However, the demand for insights and analytics is urgent. An IDC study found 79% of enterprises use over 100 data sources, and 37% of data leaders say they are “barely keeping the lights on,” managing complexity instead of driving innovation.

The medallion architecture provides a solution to efficiently turn raw data into clear, ready-to-analyze information while maintaining control and automation.

While traditional implementations can take weeks or months, the right tools and structure can enable setup in just a couple of days. Nexla’s no-code Nexsets offer a way to simplify implementation by auto-inferring schemas, applying validation, and performing transformations through a click-based interface.

This blog will walk you through what medallion architecture is and outline best practices for managing the bronze, silver, and gold layers. It will also show you how to implement a modern, automated stack in just 48 hours using Nexla.

What is Medallion Architecture?

A Medallion Architecture is the de facto standard for structuring analytics-ready data in a lakehouse. It enables organizations to handle diverse and voluminous data sources with scalability and governance, while delivering trusted insights. Medallion architecture breaks data down into three refinement stages:

• Bronze Layer: The initial landing zone where raw, unprocessed data from sources like databases, APIs, or flat files is ingested and stored in its native format. It preserves the original structure, ensuring no data is lost.
• Silver Layer: This layer cleans, standardizes, and enriches data from the bronze layer, making it reliable for downstream processing. It focuses on data quality and consistency.
• Gold Layer: The final layer delivers curated datasets optimized for business intelligence, AI, and reporting. Business logic and aggregations are applied to create datasets tailored for specific analytical needs.

Medallion Architecture Best Practices for Managing Bronze, Silver, and Gold Layers

When building a medallion data architecture, each layer must be designed with a specific purpose to preserve raw data, enforce quality, and optimize for business use. Organizations can build a medallion data architecture that scales with growing data demands by following the best practices given below. 

Bronze Layer Best Practices

The bronze layer ingests data exactly as it arrives, storing each new record in an append-only manner along with metadata such as timestamps and source IDs.

• Schema Detection and Evolution: Ingest raw data while preserving all changes, use schema-on-read or schema evolution in formats like Delta or Iceberg, so new columns or types are accepted automatically.
• Data Cataloging: Capture rich metadata like source details, timestamps, batch IDs, and record counts at ingestion to enhance discoverability and reuse. 
• Error Handling: Implement quarantine mechanisms to isolate corrupted, malformed, or non-conforming records, preventing pipeline failures. 
• Audit Trails: Maintain complete tracking of ingestion processes for compliance and troubleshooting. 

Silver Layer Best Practices

The silver layer transforms raw data into a standardized, reliable state, preparing it for advanced processing and analytics.

• Data Quality Gates: Apply automated validation rules, such as null checks, type consistency, and value ranges, to ensure data integrity. Nexla’s no-code Rules panel allows users to define quality checks through a click-based interface, allowing them to catch errors early.
• Standardization Protocols: Enforce consistent naming conventions and data types across sources for clean, uniform structures. 
• Deduplication Strategies: Identify and resolve duplicate records to maintain accuracy. Clean data enhances reliability and avoids double-counting in analytics environments.
• Change Data Capture (CDC): Process only incremental or updated records from the Bronze layer, improving efficiency and keeping the Silver layer fresh with minimal reprocessing.

Gold Layer Best Practices

The gold layer applies unified business logic aggregations, KPI calculations, and dimensional modeling right in Gold to maintain a trusted source of truth.

• Business Logic Centralization: Create a single source for calculations such as churn rate, customer lifetime value, or financial ratios within Gold layer transformations. This helps maintain consistent logic across reports and teams, such as ensuring finance and product teams use the same churn rate definition. For example, without centralization, the finance team might define churn based on inactive accounts, while the product team might focus on app drop-offs, causing conflicting data. Central management in the Gold layer ensures a single truth, consistent dashboards, and easier updates as business needs change.
• Performance Optimization: Use partitioning, indexing (like Z-order), and columnar formats to accelerate queries. Consider materialized views or data marts for heavy analytic workloads.
• Access Control: Implement role-based permissions to secure data access for different user groups.
• SLA Management: Define and enforce SLAs for freshness and availability. For example, set automated refresh schedules for each layer, such as hourly Bronze ingestion, daily Silver validation, and nightly Gold updates, to ensure data stays within acceptable latency windows. Monitor ingestion, processing lags, and alert on SLA breaches.

Building a Modern Medallion Architecture in 48 Hours

Traditional medallion architecture implementations often require weeks or even months due to complex coding, manual configurations, and integration challenges. However, with the right platform like Nexla, you can deploy a full Medallion pipeline in just two days.

Below is a detailed timeline for building your modern data stack in 48 hours, going from raw data to the Gold layer, operational, and governed.

Day 1 (First Half): Foundation and Bronze Layer Setup

Establishing a robust foundation for raw data ingestion is critical for the medallion data architecture’s success. The following lists the steps for setting up the bronze layer.

1. Connect and Ingest Raw Sources: Integrate source systems, such as databases, REST APIs, file systems, message queues, or event streams. This forms the raw ingestion layer, capturing all incoming data in its original format.
2. Auto-Create Bronzed Nexsets: Ingested data should be stored in append-only Bronze tables or files, preserving full fidelity, schema fidelity, and all ingestion metadata (timestamps, source IDs, file names).
3. Configure Quality & Quarantine: Apply initial validation, including null checks and type enforcement. Route invalid records to quarantine buckets for later inspection, ensuring pipeline resilience.
4. Enable Rich Metadata Capture: Ensure each record includes source-specific metadata. The recorded history of changes allows for complete tracking and the ability to revert to previous updates.

Day 1 (Second Half): Silver Layer Preparation

Prepare the silver layer to clean, standardize, and enrich data, ensuring reliability for downstream analytics. Follow the steps below for a robust silver layer.

1. Create Cleaned Silver Datasets: Transform Bronze inputs into Silver by applying “just-enough” cleaning techniques. These techniques include type enforcement, standard naming, null handling, and basic data harmonization. 
2. Standardize and Deduplicate: Normalize field formats, such as dates and times, and enforce consistent naming conventions. Deduplicate data using business keys, such as customer IDs or transaction numbers.
3. Implement Incremental Loads (CDC): Use Change Data Capture or delta processing to load only new or changed records for improved efficiency and freshness.
4. Test and Validate Flow: Run sample data through transformations to confirm that validation rules trigger correctly and that quarantines are operational.

Day 2: Silver and Gold Layer Implementation

Complete the medallion architecture by finalizing the silver layer and building gold datasets as outlined below.

• Harden Silver Pipelines: Enhance quality rules, value ranges, referential integrity, and advanced deduplication. Modularize pipelines by source to isolate failures and simplify maintenance.
• Define Canonical Models: Construct conformed schemas for entities such as customers, transactions, or events, enabling cross-source joins and unified analytics.
• Build Gold Datasets: Transform Silver data into Gold tables built for business use, like star schemas, KPIs, and dimensional models for reporting.
• Optimize Performance: Use columnar formats like Delta or Iceberg, employ thoughtful partitioning, indexing, and data layout strategies to accelerate querying and reduce compute costs.
• Govern & Publish: Register Gold datasets in a central catalog or marketplace. Apply role-based access controls and SLA monitoring for data freshness and availability.

You can deliver a traceable analytical pipeline in under 48 hours by following this structured approach: ingestion, cleansing, modeling, and governance.

Real-World Examples and Case Studies 

Nexla empowers organizations across industries to implement medallion architectures, turning data challenges into opportunities for growth. Companies achieve faster onboarding, improved efficiency, and trusted data for analytics by using Nexsets and the Data Product Marketplace. 

We will discuss two examples of how Nexla impacts insurance and retail.

Insurance Case Study

A major U.S. insurance provider faced integration and data transformation challenges while handling claims for non-traditional and third-party policies. These challenges led to slow partner onboarding and manual data workflows. 

Using the Nexla integration platform, they streamlined ingestion, applied automated validations, and delivered analytics-ready datasets. As a result, partner onboarding was accelerated by 3.5 times (from months to days), manual data work decreased by 70%, and claims processing efficiency improved by 30%. Nexla’s built-in PII detection ensured HIPAA compliance and made data secure and accessible.

Retail Case Study

Online grocery marketplace Instacart, operating in North America, manages data from over 85,000 store locations. They needed to scale their data integration for retail partners. Nexla’s universal connector allowed Instacart to automate the pipeline-building process. It transferred accurate product and pricing data from retail partners into Instacart’s database with ease.

As a result, Instacart reduced launch times by up to two months and cut maintenance work by 5 times. This supported a 748% increase in retail partners, enabling scalable growth and real-time analytics for operational efficiency.

Key Performance Indicators (KPIs) to Track  

A successful medallion architecture delivers measurable value, from faster data onboarding to reliable analytics. Tracking the right KPIs ensures your data operations align with business goals, providing visibility into quality and scalability. Here are the critical KPIs to track for your medallion architecture.

Onboarding Speed: Time-to-First-Ingestion

Measure how quickly a new data source moves from connection to Bronze layer ingestion. Best-in-class systems can achieve results in days or hours, compared to traditional timelines of weeks or months.

Reprocessing Reduction

Calculate the decrease in manual pipeline reruns. Effective layering minimizes these by capturing raw data in Bronze, quarantining bad records in Silver, and preventing broken jobs from propagating upstream.

Freshness SLA: Data Latency

Track the time lag between data arrival and the availability of the Gold layer. Pipelines should be securely controlled, using incremental loads and CDC, to target latency windows of minutes to an hour according to business needs.

Scalability: Diverse Source and Volume Support

Monitor the number and type of sources onboarded, as well as throughput volume. A flexible architecture should scale from dozens to hundreds of heterogeneous sources without requiring manual re-engineering.

Data Quality Compliance

Track error rates, quarantine volumes, and validation pass rates within each layer. Rising exceptions can indicate upstream schema drift or instability in the source. The quality of the gold layer should consistently meet the defined business thresholds.

Performance & Cost Efficiency

Evaluate query performance metrics such as average runtime and resource consumption for Gold datasets. Use partitioning and indexing to minimize compute costs while delivering fast analytics, and aim for sub-second responses on common queries.

Data Products in Medallion Architecture with Nexla 

Virtual data products are the unifying force in a modern medallion architecture like Nexla’s Agile Medallion Architecture that transforms complex pipelines into reusable and self-documenting assets. Nexla’s Nexsets transform raw data into data products for seamless integration across layers for agility at every step. It offers distinct advantages within the medallion architecture:

• Reusable and Format‑agnostic: Nexsets abstracts heterogeneous sources into a standardized interface that can be used across pipelines and integration styles.
• Governed and Self‑Documenting: Each Nexset carries built-in metadata, including inferred schemas, lineage, sample records, annotations, and version history. This ensures that every dataset remains discoverable, traceable, and audit-ready.
• Metadata‑Driven: Nexsets use smart metadata intelligence to detect schema changes, adjust validation rules automatically, and keep data products up-to-date without manual rework.

How Nexla Automates Each Layer

Nexla’s platform streamlines the creation of data products within the medallion architecture, ensuring both speed and scalability:

• Bronze: Upon ingesting raw sources, be it APIs, unstructured files, or databases, the system auto-scans formats and generates Bronze Nexsets. This process preserves source fidelity and metadata.
• Silver: Through no-/low-code rule editors like Nexset Designer, you can apply lightweight transformations like cleansing, deduplication, type standardization, and validation, and enrich your data. Each rule and outcome is logged within the Silver Nexset, building trust in data quality.
• Gold: Silver Nexsets feed into Gold-level data products, where business logic is applied. These include aggregations and publishing to Nexla’s Data Product Marketplace. These are optimized for analytics consumption, governed via access controls and SLA tracking.

Download our Practical Guide to Data Integration now to start accelerating your Medallion Architecture rollout without extensive coding.