Based on a customer case study, an advanced tutorial on using Delta Live Tables to process JSON schema evolution without requiring to restart
Disclaimer: I am a solutions architect at Databricks. The views and opinions expressed in this article are my own and do not necessarily reflect those of Databricks.
Schema evolution is a common phenomenon in the world of data engineering. When extracting data from sources and loading it into a destination, changes in the source schema are inevitable. This challenge is amplified when dealing with source systems that include JSON payloads, such as JSON-type columns in PostgreSQL. The likelihood of schema changes within these JSON payloads is high — new fields can be added at any time, often deeply nested at various levels. These frequent changes significantly increase the complexity of building robust data pipelines that parse such schema changes and evolve the schema seamlessly.
The Databricks Intelligence Platform, powered by the Delta Lake format, offers robust support for schema evolution, ensuring flexibility and resilience when dealing with changes in data structure. Delta Lake can…