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Jen_Evangelista

+source https://docs.databricks.com/aws/en/query/formats/binary

Jen_Evangelista

hI, @kapil_dua., to isolate the UI issue, first verify the source schema directly in a notebook:df = (spark.read.format("binaryFile").option("pathGlobFilter", "*.pdf").load("/Volumes/<catalog>/<schema>/<volume>/<folder>/"))df.printSchema()The binaryF...

Jen_Evangelista

Hi, @turagittech ,Your source-to-target comparison is a good starting point. Check missing inserts, changed values, and records deleted from the source separately. A source-to-target left join alone can miss deletes.Since you mentioned a full refresh...

Jen_Evangelista

HI, @Raj_DB, Lakehouse Federation with a Snowflake connection and a foreign catalog fits this requirement.After configuring those objects, you can query from a notebook:df = spark.sql("""SELECT *FROM snowflake_catalog.sales.ordersWHERE order_date >= ...

Jen_Evangelista

Hi @ChristophIRE,Building on the replies above, here is a rollout checklist for the tag-based ABAC approach, with code.Coverage check per tableFor each table, record which sensitive columns match the policy and where the entitlement key comes from. A...

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