You've built solid data architectures, but here's a reality check: business requirements constantly change, and your data schemas must evolve with them.
Adding new fields, changing data types, or restructuring tables can break every downstream system that depends on your data.
Engagement Message
What is one issue that can arise when a new column is added without planning?
Schema evolution is the challenge of changing data structures without breaking existing applications. When marketing adds a new customer field, your analytics dashboards shouldn't stop working.
This becomes critical in production environments where dozens of teams depend on your data structures.
Engagement Message
What could happen if changing a table structure broke all existing reports?
The key principle is backward compatibility - new schema versions must work with applications built for older versions. Adding optional fields is safe, but removing or renaming fields breaks everything.
Think of it like updating a building's blueprint while people are still living in it.
Engagement Message
Why would removing a field from a schema cause problems for existing applications?
Forward compatibility is the reverse challenge - ensuring old data works with new applications. This matters when you have historical data that doesn't contain newly required fields.
Your 2020 sales data might not have the "customer_tier" field added in 2023, but new reports still need to process it.
Engagement Message
How would you handle missing fields when processing historical data?
Apache Avro excels at schema evolution by embedding schema information directly with the data. When you read Avro files, they contain both the data and the schema used to write it.
This lets readers automatically handle schema differences without manual intervention.
Engagement Message
