Schema Evolution is the process of modifying the structure of a dataset (its schema) over time to accommodate changing business requirements, data sources, or analytical needs. It ensures that data systems remain flexible and adaptable while maintaining data integrity and compatibility. Hereβs a detailed breakdown of Schema Evolution:
Schema Evolution involves:
Additive Changes:
middle_name
column to a customer table.Subtractive Changes:
phone_number
column.Modifying Changes:
date
field from string
to timestamp
.Renaming Changes:
cust_id
to customer_id
.Delta Lake:
Apache Avro:
E-Commerce:
loyalty_points
column to a customer table to track rewards.Healthcare:
vaccination_status
field to patient records.Finance:
transaction_amount
field from integer
to decimal
.IoT:
sensor_type
field to sensor data to support new devices.