Reviewed by Sumit Sarabhai Fetching a million rows from SQL Server into a Polars DataFrame used to mean a million Python objects, a million GC allocations, and then throwing it all away to build a DataFrame. Not anymore. mssql-python now supports fetching SQL Server data directly as Apache Arrow structures - a faster and more memory-ef...

Reviewed by: Sumit Sarabhai If you've been writing SQL in Python, you already know the debate: positional parameters (?) or named parameters (%(name)s)? Some developers swear by the conciseness of positional. Others prefer the clarity of named. With mssql-python, you no longer need to choose - we support both.  We've added dual parameter sty...

Reviewed by: Sumit Sarabhai and Gaurav Sharma If you’ve worked with databases in Python, you know the boilerplate: open a connection, create a cursor, run queries, commit or rollback transactions, close cursors and connection. Forgetting just one cleanup step can lead to resource leaks (open connections) or even inconsistent data. That’s whe...

Reviewed by Imran Masud and Sumit Sarabhai When it comes to working with Microsoft SQL Server in Python, pyodbc has long been the de facto driver. It's mature, trusted and has been serving the community well for years. But as applications scale and Python becomes more central to modern data workflows — from microservices to data engin...

After the successful release of Public Preview of mssql-python driver, we’re thrilled to announce a major milestone for the mssql-python driver: full support for all three major operating systems—Windows, macOS, and Linux. This release marks a significant leap forward in our mission to provide seamless, performant, and Pythonic connectivity to ...