Writing chunked text into Delta Lake tables in Unity Catalog

After splitting source documents into chunks, the chunks must be loaded into a Spark DataFrame and written as a managed Delta table registered in a Unity Catalog catalog and schema, typically to serve as the source table for a downstream Vector Search index. The write step uses standard Spark/Delta APIs but requires attention to UC three-level naming, schema design, and Change Data Feed settings.

1 · Learn the must-know

• 1Convert the list of chunked text (with associated metadata like source, chunk id) into a Spark DataFrame before writing, e.g. via spark.createDataFrame().
• 2Write the DataFrame to a Delta table using the three-level Unity Catalog namespace catalog.schema.table, e.g. df.write.mode("overwrite"/"append").saveAsTable("catalog.schema.table").
• 3Include a unique primary-key/id column per chunk row, since Databricks Vector Search indexes require a designated primary key column on the source Delta table.
• 4Enable Change Data Feed on the table (TBLPROPERTIES (delta.enableChangeDataFeed = true)) if the table will sync incrementally to a Vector Search index.
• 5The user/service principal must have USE CATALOG, USE SCHEMA, and CREATE TABLE privileges on the target Unity Catalog location before the write will succeed.
• 6Prefer append or merge for incremental chunk ingestion and overwrite only for full reprocessing, to avoid breaking downstream index sync or duplicating chunks.

2 · Check your understanding