We are currently in the process of migrating a large number of SQL Server (SSMS)–based workloads to Microsoft Fabric Spark SQL / Lakehouse notebooks. In SQL Server, most of our legacy queries rely on case‑insensitive collations (for example, SQL_Latin1_General_CP1_CI_AS). As a result, string comparisons such as attribute names, option‑set values, and metadata fields work seamlessly regardless of casing (camel case, upper case, lower case). In Fabric Spark SQL, however: String value comparisons are case‑sensitive by default The spark.sql.caseSensitive setting only affects column names, not string data There is currently no way to define or enforce a case‑insensitive collation similar to SQL Server Because of this difference, when migrating existing SSMS queries to Fabric: Queries that worked correctly for years now fail silently or return no data Every affected comparison must be manually rewritten using LOWER() / UPPER() This change needs to be applied across hundreds of views, CTEs, and joins The manual effort is significant and error‑prone, especially for large enterprise DW migrations From a migration and maintainability standpoint, this creates a major challenge: It substantially increases migration effort and timelines It introduces risk of missed transformations and data quality issues It makes direct parity between SSMS and Fabric difficult to achieve Request / Ask We would like to understand if Microsoft Fabric has: A roadmap item to support case‑insensitive collations (or equivalent behavior) in Spark SQL A recommended platform‑level configuration or abstraction to handle SSMS‑style case‑insensitive comparisons Any best‑practice guidance or tooling to reduce the manual refactoring effort during large‑scale migrations Even a feature such as: Session‑level or notebook‑level case‑insensitive string comparison, or Table/view‑level collation behavior, or A Fabric‑native compatibility mode for SQL Server migrations would significantly ease enterprise adoption and migration workloads. We would appreciate your guidance on the best approach going forward and whether this limitation is expected to change in upcoming Fabric releases. Thank you for your support. Best regards, Naveen
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