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Architecture of Data Platform

Data architectures often evolve organically in small companies. Someone creates a report, others copy and modify it, and soon, you have a tangled web of undocumented transformations and conflicting metrics. This approach might work initially but inevitably leads to data quality issues, slow development cycles, and frustrated users. As your company grows, the cost of this technical debt grows.

Common Scaling Challenges

Small companies often hit several predictable scaling walls. One is when data volume exceeds what a simple database can handle efficiently. Another appears when combining data from multiple sources for complex analysis. Another emerges when different teams need different views of the same data. Without a proper architecture, each of these challenges leads to band-aid solutions that make the next challenge even more complicated to solve.

The Hidden Costs of Poor Architecture

Poor architecture creates costs that aren’t immediately visible but compound over time. Data scientists spend more time cleaning data than analyzing it. Engineers repeatedly solve the same problems because solutions aren’t reusable. Business users lose trust in reports because different queries return different results. Perhaps most expensively, decision-making slows as people spend time debating whose numbers are correct instead of taking action.

When to Implement a Formal Architecture

If your analysts spend more time reconciling numbers than analyzing them, that’s a sign. If your data team is constantly firefighting instead of building new capabilities, that’s another. If business users have stopped trusting the data, that’s a critical signal.

The Medallion Architecture

The Medallion Architecture divides data processing into three layers: Bronze, Silver, and Gold. Each layer has a specific purpose and clear boundaries. This separation helps your data platform be more manageable and reliable.

Benefits of Layered Approach

The layered approach provides several immediate benefits. Data quality improves because each layer has specific validation requirements. Development speed increases because teams can work independently on different layers. Most importantly, when issues arise, you can trace them to particular layers rather than searching through a tangled web of dependencies.

Implementation Trade-offs

While the Medallion Architecture brings many benefits, it requires some trade-offs. Development initially takes longer because you’re building proper foundations rather than quick fixes. Query paths become longer because data flows through multiple layers. Teams need to learn new patterns and break old habits. However, these short-term costs prevent much larger long-term problems.

Bronze Layer: Data Foundation

The Bronze layer stores data precisely as it arrives from source systems. This unmodified data serves as the foundation for all downstream processing. This raw data copy allows you to reprocess data if issues emerge in downstream transformations.

Raw Data Storage

Raw data arrives in various formats - JSON from APIs, export CSVs, database dumps, and streaming events. Store each format appropriately, preserving all fields and metadata. Even fields you think are useless might become valuable later. For streaming data, preserve event order and timestamps.

Initial Profiling

Profile incoming data to understand its characteristics. Track record counts, schema changes, and value distributions. These profiles help detect issues early. For example, if a critical field suddenly contains mostly nulls, you want to know before this affects reports.

Handling Sensitive Data

Identify and tag sensitive data as it enters the Bronze layer. This includes personally identifiable information (PII), protected health information (PHI), and financial data. Once tagged, you can implement appropriate security controls and ensure compliance with regulations like GDPR, CCPA, and HIPAA.

Bronze Layer Data Cleaning

Data cleaning is performed in the Bronze layer in addition to the raw format. Standardize formats (dates, phone numbers, addresses). Remove duplicates. Handle missing values. Document your cleaning rules—they’re part of your business logic.

Silver Layer: Integration and Standardization

The Silver layer transforms raw data into a clean, standardized form. This is where you resolve source inconsistencies, apply business rules, and create a coherent view of your data.

Most business users won’t interact with this layer, but data scientists spend a lot of time in this layer, so data quality and significant documentation are required.

Silver Layer Data Cleaning

While Bronze looks at tables individually, Silver is where the tables come together. Do we lose records on joins? Are the orphaned records with an ID that doesn’t match a base table? Join tests start in this layer.

Integration Patterns

Different sources often represent the same concepts differently. Customer IDs might be integers in one system and GUIDs in another. Products might have different SKUs in other systems. The Silver layer resolves these differences, creating a single, consistent view.

Handling Schema Changes

Sources change their schemas. New fields appear, old fields disappear, and data types change. The Silver layer must handle these changes gracefully. Version your transformations and maintain backward compatibility where possible.

Gold Layer: Business Value

The Gold layer presents business-ready data. This layer is where you create the views and aggregations that power reports and dashboards. This layer is the only one most business users access directly.

Metric Definitions

Define metrics clearly and consistently. Revenue, customer counts, and product metrics should match across all reports. Document the logic behind each metric. When definitions change, version them clearly and communicate the changes to users. The changes should exist in a single place, so the logic changes in precisely one place. The change should be backward compatible and allow end users to see different versions simultaneously.

Report Optimization

Structure Gold layer tables for reporting performance. Pre-aggregate common metrics. Create appropriate indexes. Consider materializing frequently accessed views. The goal is to make common queries fast and efficient.

Access Patterns

Different teams need different views of the same data. Finance needs revenue by accounting periods. Marketing needs customer segments. Product needs user behavior analysis. Design your Gold layer to support these various needs without requiring users to write complex queries.

Implementing Incrementally

You can build some over time. Start with one critical data flow and implement it through all three layers. This approach tests the architecture and delivers immediate value.

Starting Small

Pick a bounded project with clear value. For example, start with core financial metrics or key product analytics. Build the complete pipeline for this subset, from Bronze through Gold. Use this as a template for future work.

Migration Strategy

Run old and new systems in parallel initially. Compare results to ensure accuracy. Migrate users gradually as they gain confidence in the new system. Document everything - this is your playbook for future migrations.

Managing Parallel Systems

During migration, you’ll have parallel systems running. Be clear about which system is authoritative for which metrics. Set clear timelines for deprecating old systems. Monitor usage to ensure migration is progressing.

Performance Optimization

Performance isn’t just about fast queries. It’s about balancing speed, cost, and maintainability. Good performance comes from good design more than from tweaking individual queries.

Partitioning Strategies

Partition large tables on commonly filtered columns like date or customer segment. This reduces the amount of data scanned for typical queries. But don’t over-partition - too many small partitions can hurt performance.

Materialized Views

Materialized views can dramatically improve query performance by pre-calculating common aggregations. However, they add complexity and cost. Use them for frequently accessed metrics where real-time accuracy is not required.

Query Optimization

Write queries with the warehouse architecture in mind. Push filters to outer queries. Use appropriate join types. Avoid SELECT *. But don’t over-optimize - readable queries are often more valuable than slightly faster ones.

Special Considerations

Real-time Data Needs

Most business metrics don’t need real-time updates. Consider whether delays of 5 minutes are sufficient or the SLA must be shorter. Shorter time requirements add significant complexity and cost.

Managing Storage Costs

Storage is usually cheaper than people think. Don’t compromise data quality to save storage. Instead, focus on:

Backup Strategies

Your Bronze layer is your primary backup. It contains all raw data and can rebuild other layers if needed. Still, maintain point-in-time backups of critical Gold layer tables - recreating them from Bronze can be time-consuming.

Disaster Recovery

Document restore procedures for each layer. Bronze layer restores might pull from source systems. Silver layer restores might rebuild from Bronze. Gold layer restores might use backups or rebuilds from Silver.

Practical Examples

Customer Data Integration

A typical first implementation combines customer data from multiple sources:

The Bronze layer maintains separate tables for each source. The Silver layer resolves customer identities and creates a unified view. The Gold layer presents customer segments and metrics.

Financial Reporting

Financial data requires special care:

Product Analytics

Product data often combines multiple streams:

The Bronze layer captures raw events. The Silver layer sessionizes and enriches. The Gold layer creates product metrics and funnels.

Integration Points

Application Integration

Applications should write directly to Bronze layer tables when possible. This ensures:

ETL/ELT Processes

Modern data platforms prefer ELT (Extract, Load, Transform) over ETL:

Third-party Tools

Many tools want direct database access. Instead:

Planning for Scale

Plan for 10x your current volume, but don’t build for 100x. This means:

Schema Evolution

Schemas will change. Plan for it:

Tool Migration

You will change tools over time:

Capacity Planning

Monitor system usage to plan capacity:

Conclusion

Architecture isn’t about building the perfect system. It’s about building a system that can evolve with your business while maintaining data quality and user trust. The Medallion Architecture provides a framework that scales with your needs:

Remember: good architecture enables growth and change. Poor architecture forces constant firefighting. The time you invest in proper architecture will pay off many times over as your company grows.