method#Data#Analytics#Database Evolution#Risk Management

Schema Evolution Strategy

A method for continuously adapting and improving database schemas with minimal disruptions.

The Schema Evolution Strategy enables organizations to implement database changes gradually and with minimal risk while maintaining the stability of current systems.

Maturity

Emerging

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeDesign
Organizational maturityIntermediate

Technical context

Integrations

API for External Data SourcesCloud DatabasesETL Tools for Data Migration

Principles & goals

Principles

Iterative WorkFlexibilityDocumentation of Changes

Value stream stage

Build

Organizational level

Team, Domain

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Data Loss During Migration
Incompatibility with Existing Applications
Insufficient Testing

Best practices

Regular Quality Checks of Data
Iteration of Adjustments Based on Feedback
Updating Documentation to Reflect New Changes

I/O & resources

Inputs

Documentation of Current Data Architecture
Requirements for Database Adjustment
Test Scripts for Migrations

Outputs

Implementation Report
User Feedback
Updated Database Documentation

Resources

Description

The Schema Evolution Strategy enables organizations to implement database changes gradually and with minimal risk while maintaining the stability of current systems. This method promotes efficiency and flexibility in data processing.

✔Benefits

Minimizing Downtime
Improved Adaptability
Higher Data Quality

✖Limitations

Possible Complexity with Larger Changes
Dependency on Existing Systems
Resource-Intensive

Trade-offs

Metrics

Change Velocity
The speed at which changes are made to the database.
Error Rate in Migrations
The rate of errors occurring during database migrations.
Customer Satisfaction
The satisfaction rate of users with the changes.

Examples & implementations

Example Company A

This company implemented the Schema Evolution Strategy to adjust its databases for a new service.

Example Company B

By applying this method, the company was able to adjust its database structures without downtime.

Example Company C

The company significantly improved data quality using this method.

Implementation steps

Planning Schema Evolution

Conducting Training for the Team

Monitoring the Implementation

⚠️ Technical debt & bottlenecks

Technical debt

Insufficient Documentation of Changes
Outdated Database Technologies
Technical Debt from Unplanned Changes

Known bottlenecks

Technical DebtLack of DocumentationOutdated Technologies

Misuse examples

Database Changes Without Prior Testing
Forgetting Backups Before Migrations
Changes That Are Not Documented

Typical traps

Making Too Many Changes at Once
Ignoring Stakeholder Feedback
Lack of Training for the Team

Required skills

Knowledge in Database DesignExperience with Data MigrationSkills in Project Management

Architectural drivers

Scalability of Data ArchitectureFlexibility in DevelopmentCompliance with Industry Specifications

Constraints

• Resource Budget
• Technical Infrastructure
• Compliance Requirements