[SPARK-58940][SDP][DOC] Add migration notes for Declarative Pipelines case sensitivity

docs/sql-migration-guide.md

@@ -48,6 +48,8 @@ license: |
 - Since Spark 4.3, the Spark-recognized time-travel options (`versionAsOf` and `timestampAsOf`, or the keys configured by `spark.sql.timeTravelVersionKey` and `spark.sql.timeTravelTimestampKey`) are rejected with `UNSUPPORTED_FEATURE.TIME_TRAVEL` on catalog-backed Data Source V2 writes, including table creation and replacement through `DataFrameWriterV2`, because writes must target the current table state rather than a historical version. Previously, Spark passed them to the connector as ordinary write options.
 - Since Spark 4.3, `HAVING` is evaluated before window functions when the `SELECT` list also contains generator functions such as `explode`. Previously, window functions could include groups removed by `HAVING` and produce incorrect results.
 - Since Spark 4.3, the Spark Connect session errors `INVALID_HANDLE.SESSION_CHANGED`/`SESSION_CLOSED`/`SESSION_NOT_FOUND` carry SQLSTATE `08003` instead of `HY000`; the condition names are unchanged. Code matching these errors on SQLSTATE should match `08003` or class `08`.
+- Since Spark 4.3, [Declarative Pipelines](declarative-pipelines-programming-guide.html) honors `spark.sql.caseSensitive` when inferring and evolving pipeline table schemas. Under case-insensitive resolution (the default), column names that differ only in case now identify the same column: flows writing to one table contribute a single column rather than one per spelling, and a column that differs only in case from one already persisted in the target is written to that column instead of being added alongside it. Previously such names were always treated as distinct, producing a table schema that Spark's own resolver could not disambiguate and that could fail later with errors such as `COLUMN_ALREADY_EXISTS` or `AMBIGUOUS_REFERENCE`. When two flows' columns fold together but their types are incompatible, the update now fails at validation with `UNABLE_TO_INFER_PIPELINE_TABLE_SCHEMA`. Where the spellings differ, the surviving one comes from the flow with the lowest identifier. An explicitly declared table schema keeps its spelling over the inferred one. Incremental streaming tables keep the persisted spelling of an existing column; materialized views re-infer the schema on every update. Set `spark.sql.caseSensitive` to `true` to keep names differing only in case distinct, as before.
+- Since Spark 4.3, all flows writing to the same pipeline table must agree on the effective `spark.sql.caseSensitive`, which each flow takes from its own SQL configuration (a `SET` in pipeline source, which never reaches the session) and otherwise from the session. A disagreement fails the update with `CONFLICTING_PIPELINE_FLOW_CASE_SENSITIVITY`, because that value decides whether names differing only in case identify the same column and would otherwise make the table's schema depend on the order the flows are evaluated in. Set `spark.sql.caseSensitive` to the same value for every flow writing to the table, remembering that a flow which does not set it inherits the session's value.
 
 ## Upgrading from Spark SQL 4.1 to 4.2