The Schema Evolution Challenge in Modern Data Pipelines (Part 1/5)

The Problem: Broken Pipelines from Schema Changes

Three weeks into my role at Globalmart, an e-commerce company handling millions of daily transactions, I received a 2 AM call from our BI lead. The pipeline had failed because a source system added a new loyalty_points column to the customer data feed. By the time I fixed it, executive reports were three hours late.

This wasn't an isolated incident. We faced a recurring pattern:

1. Source systems changed schemas without notice

2. Pipeline broke

3. Engineers scrambled to fix it manually

4. Business teams missed critical data for decisions

Over six months, we experienced 59 pipeline failures due to schema changes—nearly 10 per month.

Globalmart's Data Landscape

Globalmart's data came from multiple systems:

• POS systems from 200+ retail locations

• E-commerce platform

• Inventory management

• CRM tools

• Third-party logistics

Each system was owned by different teams who often changed data structures without informing the data team.

The data model included interconnected entities: Customers, Orders, Products, Payments, Returns, and Shipping. Complexity lay not just in the number of tables but in their relationships—a single order involved updates across multiple entities.

Medallion Architecture Implementation

Globalmart used a medallion architecture in Databricks on GCP:

1. Bronze Layer: Raw data landing

2. Silver Layer: Validated, cleansed data with business rules

3. Gold Layer: Aggregated, business-ready data

┌───────────────┐     ┌────────────────┐     ┌───────────────┐
│  Bronze Layer │     │  Silver Layer  │     │  Gold Layer   │
│  Raw Ingestion│────►│  Cleaned Data  │────►│  Business     │
│               │     │                │     │  Ready Data   │
└───────────────┘     └────────────────┘     └───────────────┘

The pipeline assumed stable data structures—an assumption that proved problematic.

Impact Assessment

Schema changes took many forms:

• Addition of new columns

• Removal of columns

• Data type changes

• Column renaming

• Restructuring of nested fields

The business impact was significant:

The real business costs included:

• Financial Impact: ~$20,000 per day for major reporting delays

• Engineering Overhead: 15% of engineers' time spent fixing schema issues

• Lost Trust: Decreasing confidence in the data platform

• Scaling Problems: Growing frequency and severity of issues

During one sales event, a new promotional code field broke the pipeline, causing a six-hour analytics delay. The marketing team couldn't optimize ad spend in real-time, wasting an estimated $150,000.

Failed Traditional Approaches

Our initial attempts at solutions weren't effective:

1. Strict Schema Enforcement: Rejected non-conforming data, creating historical gaps

2. Manual Schema Updates: Couldn't keep pace with change frequency

3. Schema Validation Jobs: Still reactive rather than proactive

These approaches failed because:

• Coordinating changes across dozens of source systems was nearly impossible

• Manual updates couldn't scale

• Strict enforcement sacrificed data continuity

Delta Lake as a Potential Solution

Delta Lake offered promising capabilities for our schema evolution challenges:

1. Automatic Schema Evolution: Adapting to changes without intervention

2. Schema Enforcement: Maintaining data quality while allowing evolution

3. Time Travel: Accessing historical data versions

4. Transaction Log: Tracking all table changes

Our ideal solution needed to:

• Automatically adapt to most schema changes

• Maintain data integrity

• Preserve historical consistency

• Minimize processing latency

• Provide visibility into schema changes

• Maintain backward compatibility

The Broader Challenge

As we analyzed further, we recognized schema evolution was part of a larger data engineering challenge. We needed to address:

1. Incremental Processing: Full table scans were becoming prohibitively expensive

2. Pipeline Orchestration: Managing processing dependencies

3. Data Quality: Ensuring consistency across evolving schemas

Coming Next

In Part 2, we'll look into the implementation of schema evolution with Delta Lake on Databricks, covering:

• Schema evolution mechanisms

• Handling different types of schema changes

• Effective code patterns

• Testing and validation approaches

Stay tuned for Part 2: Implementing Schema Evolution with Delta Lake on Databricks.