Release Announcement of SQL Server Migration Assistant (SSMA) v10.4
We’re excited to announce the release of SQL Server Migration Assistant (SSMA) v10.4, bringing significant enhancements across all SSMA flavors to further streamline your database modernization journey to Azure SQL and SQL Server. This release focuses on deeper platform support, expanded schema conversion capabilities, and new AI-assisted experiences—with more coming soon Support for SQL Server 2025 (Available across all SSMA flavors) With SSMA 10.4, you can now fully discover, assess, convert schema, and migrate data to SQL Server 2025. This includes: Target selection for SQL Server 2025 in the assessment report Updated conversion rules for new SQL Server 2025 features (more to come in future versions) Data migration pipeline compatibility Improved schema and datatype mappings Whether you are modernizing on-premises workloads or planning a hybrid Azure strategy, SSMA 10.4 ensures you're ready for the next SQL Server release. General Availability of Oracle PL/SQL to SQL Code Conversion Copilot The Copilot experience for converting Oracle PL/SQL to T-SQL is now Generally Available (GA). The AI-assisted conversion engine helps accelerate application modernization by generating high-quality error handling of T-SQL from PL/SQL packages, procedures, functions, triggers, and more. We will publish a detailed blog with examples and guidance on how to make the most of this new capability. Microsoft-Managed Endpoint for Copilot Login (Preview) (Full blog coming soon) We are introducing a new Microsoft-managed endpoint that enables SSMA Copilot usage through Microsoft Entra ID authentication, without requiring customers to bring their own Azure OpenAI subscription. This feature is in Public Preview, and more details—including setup guidance—will be shared in an upcoming blog. User-Defined Datatypes (UDTs) support in SSMA for Sybase SSMA for Sybase now supports conversion of User-Defined Datatypes (UDTs) to SQL Server. This enhancement helps teams eliminate manual schema rewrites, reduces migration risk, and improves assessment coverage for complex Sybase ASE schemas. Other Improvements in SSMA 10.4 This release also includes several enhancements across flavors: Rule engine improvements in SSMA for Sybase, enabling more consistent and accurate schema conversion Performance optimizations to reduce assessment and conversion times Security and compliance updates Improved explanations and documentation for project settings to help users better understand configuration options during migration Download Links Get the latest versions of SSMA v10.4 here: SSMA for Oracle SSMA for Db2 SSMA for Sybase SSMA for Access SSMA for MySQLSybase to SQL User Defined Datatype conversionRelease Announcement of SQL Server Migration Assistant (SSMA) v 10.1
Overview SQL Server Migration Assistant (SSMA) Access, DB2, MySQL, Oracle, and SAP ASE (formerly SAP Sybase ASE) allow users to convert a database schema to a Microsoft SQL Server schema, deploy the schema, and then migrate data to the target SQL Server (see below for supported versions). What’s new? Enhanced monitoring experience for migrations using DMS [ SSMA-Oracle] Migrating Oracle workloads using Azure Database Migration service is in preview, please refer for more details. For Oracle workloads migrating using DMS in SSMA, we are bringing an enhanced monitoring experience through tabular user interface where the users can now view live list of migrations that are in progress or completed phase. Each entry represents a migration activity along with the start time of migration, DMS used for migration and status. Users can view quick brief migration information of individual tables to know more information like Table name, Schema name, copy duration, No. of Rows copied & status real-time. For the sake of users requiring more granular monitoring information about their migration activity, SSMA provides a link to the Azure Database Migration service portal webpage (View Comprehensive Report) where they can view details like Data read, writes, Rows copies, Throughput along with copy duration, etc. Code Conversion Improvements [DB2, Oracle] Conversion enhancements for identity column from Db2 z/OS to SQL Server 2019 Improve conversion of Db2 stored procedure WITH RETURN clause to Azure SQL Database Improve database objects load for Db2 Appropriate error handling for conversion of identifier REPLACE(STRING, CHAR,CHAR) in Db2 Detection of CHAR length in Oracle VARCHAR2 datatype Downloads SSMA for Access SSMA for DB2 SSMA for MySQL SSMA for Oracle SSMA for SAP ASE Supported sources and target versions Source: For the list of supported sources, please review the information on the Download Center for each of the above SQL Server Migration Assistant downloads. Target: SQL Server 2016, SQL Server 2017, SQL Server 2019, Azure SQL Database, an Azure SQL Database managed instance Resources SQL Server Migration Assistant documentation
Leveraging GitHub Copilot for T-SQL Code Conversion: A Deep Dive into Code Explainability
Converting database code between different relational database management systems (RDBMS) is an essential part of database migration , especially when moving from Oracle’s PL/SQL to SQL Server’s T-SQL. Both Oracle and SQL Server have unique syntax, functions, and programming conventions that require significant adjustments when converting complex SQL queries, stored procedures, or functions. While manual conversion can be a painstaking process, GitHub Copilot, an AI-powered code assistant, can ease this burden by offering real-time suggestions and automating many aspects of the conversion. In the first part of this two-part blog, we explore how GitHub Copilot can be a powerful tool for code conversion, helping developers quickly adapt code from one language or framework to another. By providing context-aware suggestions and completions, Copilot simplifies the process of rewriting and refactoring code. In the second part, we dive deeper into Copilot’s explainability feature, showcasing how it enhances the code conversion process from Oracle to Azure SQL. Code explainability in GitHub Copilot refers to the tool’s ability to offer suggestions and comments that help developers understand what a given piece of code is doing. When dealing with database migrations, such as converting Oracle SQL code to Azure SQL (SQL Server), GitHub Copilot’s ability to explain, suggest, and refactor code can ease the transition. It can assist in making the conversion process smoother, more efficient, and less error-prone by explaining the logic behind Oracle-specific queries and suggesting corresponding changes in Azure SQL’s syntax We'll go through multiple examples, analyze the differences between the two languages, and show how GitHub Copilot handles these challenges. Understanding the Key Differences: PL/SQL vs T-SQL Before jumping into examples, it's important to understand few of the fundamental differences between PL/SQL (Oracle) and T-SQL (SQL Server): Syntax: While both are procedural SQL dialects, there are key differences in the way they handle variables, control structures, and flow control. Functions: Each platform has its own set of built-in functions (e.g., string manipulation, date handling, etc.), and these need to be mapped correctly during conversion. Error Handling: Error handling and exception management differ between the two, with PL/SQL using EXCEPTION blocks and T-SQL using TRY...CATCH. Cursor Handling: While both support cursors for iterating through results, their syntax differs. Leveraging GitHub Copilot for Oracle PL/SQL to SQL Server T-SQL Code Conversion: A Deep Dive into Complex Examples with Explainability Converting database code between different relational database management systems (RDBMS) is an essential part of database migration , especially when moving from Oracle’s PL/SQL to SQL Server’s T-SQL. Both Oracle and SQL Server have unique syntax, functions, and programming conventions that require significant adjustments when converting complex SQL queries, stored procedures, or functions. While manual conversion can be a painstaking process, GitHub Copilot, an AI-powered code assistant, can ease this burden by offering real-time suggestions and automating many aspects of the conversion. In the first part of this two-part blog, we explore how GitHub Copilot can be a powerful tool for code conversion, helping developers quickly adapt code from one language or framework to another. By providing context-aware suggestions and completions, Copilot simplifies the process of rewriting and refactoring code. In the second part, we dive deeper into Copilot’s explainability feature, showcasing how it enhances the code conversion process from Oracle to Azure SQL. Code explainability in GitHub Copilot refers to the tool’s ability to offer suggestions and comments that help developers understand what a given piece of code is doing. When dealing with database migrations, such as converting Oracle SQL code to Azure SQL (SQL Server), GitHub Copilot’s ability to explain, suggest, and refactor code can ease the transition. It can assist in making the conversion process smoother, more efficient, and less error-prone by explaining the logic behind Oracle-specific queries and suggesting corresponding changes in Azure SQL’s syntax We'll go through multiple examples, analyse the differences between the two languages, and show how GitHub Copilot handles these challenges. Understanding the Key Differences: PL/SQL vs T-SQL Before jumping into examples, it's important to understand few of the fundamental differences between PL/SQL (Oracle) and T-SQL (SQL Server): Syntax: While both are procedural SQL dialects, there are key differences in the way they handle variables, control structures, and flow control. Functions: Each platform has its own set of built-in functions (e.g., string manipulation, date handling, etc.), and these need to be mapped correctly during conversion. Error Handling: Error handling and exception management differ between the two, with PL/SQL using EXCEPTION blocks and T-SQL using TRY...CATCH. Cursor Handling: While both support cursors for iterating through results, their syntax differs. Example 1: Complex Stored Procedure Conversion Let’s start with a complex example that involves handling parameters, cursors, and error handling. We'll look at a PL/SQL stored procedure in Oracle that processes employee records and outputs their details. CREATE OR REPLACE PROCEDURE GetEmployeeDetails (emp_id IN NUMBER) IS CURSOR emp_cursor IS SELECT employee_id, first_name, last_name, hire_date FROM employees WHERE employee_id = emp_id; emp_record emp_cursor%ROWTYPE; emp_full_name VARCHAR2(100); BEGIN OPEN emp_cursor; LOOP FETCH emp_cursor INTO emp_record; EXIT WHEN emp_cursor%NOTFOUND; -- Concatenate first and last name emp_full_name := emp_record.first_name || ' ' || emp_record.last_name; -- Output employee details DBMS_OUTPUT.PUT_LINE('Employee ID: ' || emp_record.employee_id); DBMS_OUTPUT.PUT_LINE('Full Name: ' || emp_full_name); DBMS_OUTPUT.PUT_LINE('Hire Date: ' || TO_CHAR(emp_record.hire_date, 'YYYY-MM-DD')); END LOOP; CLOSE emp_cursor; EXCEPTION WHEN NO_DATA_FOUND THEN DBMS_OUTPUT.PUT_LINE('No employee found with the given ID.'); WHEN OTHERS THEN DBMS_OUTPUT.PUT_LINE('An error occurred.'); END; Oracle PL/SQL Example In Oracle, we might have a procedure that uses a cursor to fetch employee records and print them. The procedure also includes error handling for potential issues during execution. Key Points in Oracle PL/SQL: Cursor: PL/SQL uses CURSOR for iterating through rows. Concatenation: Oracle uses || for string concatenation. Error Handling: PL/SQL uses EXCEPTION blocks for handling errors such as NO_DATA_FOUND. Converting to SQL Server T-SQL with GitHub Copilot When you type the PL/SQL code into an IDE with GitHub Copilot enabled (e.g., Visual Studio Code), Copilot will start suggesting the equivalent SQL Server T-SQL code. Here’s how Copilot converts the procedure. Copilot’s Suggested SQL Server T-SQL Code CREATE PROCEDURE GetEmployeeDetails (@emp_id INT) AS BEGIN DECLARE @emp_id INT, @first_name NVARCHAR(50), @last_name NVARCHAR(50); DECLARE @emp_full_name NVARCHAR(100); DECLARE emp_cursor CURSOR FOR SELECT employee_id, first_name, last_name, hire_date FROM employees WHERE employee_id = @emp_id; OPEN emp_cursor; FETCH NEXT FROM emp_cursor INTO @emp_id, @first_name, @last_name, @hire_date; WHILE @@FETCH_STATUS = 0 BEGIN -- Concatenate first and last name SET @emp_full_name = @first_name + ' ' + @last_name; -- Output employee details PRINT 'Employee ID: ' + CAST(@emp_id AS NVARCHAR); PRINT 'Full Name: ' + @emp_full_name; PRINT 'Hire Date: ' + CONVERT(NVARCHAR, @hire_date, 23); FETCH NEXT FROM emp_cursor INTO @emp_id, @first_name, @last_name, @hire_date; END CLOSE emp_cursor; DEALLOCATE emp_cursor; BEGIN TRY -- Error handling code (example) END TRY BEGIN CATCH PRINT 'An error occurred.'; END CATCH END; Key Points in T-SQL: Cursor: SQL Server uses DECLARE CURSOR to declare a cursor, similar to PL/SQL but with different syntax for opening and fetching. Concatenation: SQL Server uses + for string concatenation. Error Handling: SQL Server uses TRY...CATCH for error handling, unlike PL/SQL’s EXCEPTION block. GitHub Copilot provides a T-SQL equivalent for an Oracle PL/SQL stored procedure. Explanation of the Conversion Process Cursor Declaration and Fetching: PL/SQL uses CURSOR with OPEN, FETCH, and CLOSE to process rows. T-SQL uses DECLARE CURSOR to define the cursor and fetches rows with FETCH NEXT. Both approaches are similar, but T-SQL requires explicit DEALLOCATE to release the cursor. Error Handling: PL/SQL uses EXCEPTION blocks to handle errors like NO_DATA_FOUND. T-SQL uses TRY...CATCH blocks to capture and handle errors, a major difference in error handling between the two systems. String Concatenation: PL/SQL uses || for concatenating strings, while T-SQL uses +. This is a simple yet crucial change when converting between the two dialects. Example 2: Complex Query with Date Handling Let’s now look at a query that involves date handling and conditional logic. These are often tricky when converting between PL/SQL and T-SQL due to differences in how each platform handles dates. Oracle PL/SQL Query Example SELECT employee_id, first_name, hire_date, CASE WHEN hire_date < TO_DATE('2000-01-01', 'YYYY-MM-DD') THEN 'Veteran' ELSE 'New Hire' END AS employee_status FROM employees WHERE hire_date BETWEEN TO_DATE('1990-01-01', 'YYYY-MM-DD') AND SYSDATE; Key Points in PL/SQL: TO_DATE: Used to convert strings into dates. SYSDATE: Returns the current system date. CASE: Used for conditional logic to categorize employees as either "Veteran" or "New Hire". Copilot’s Suggested SQL Server T-SQL Code SELECT employee_id, first_name, hire_date, CASE WHEN hire_date < CONVERT(DATE, '2000-01-01', 23) THEN 'Veteran' ELSE 'New Hire' END AS employee_status FROM employees WHERE hire_date BETWEEN CONVERT(DATE, '1990-01-01', 23) AND GETDATE(); Key Points in T-SQL: CONVERT: SQL Server uses CONVERT for date formatting, similar to Oracle’s TO_DATE, but with different syntax and style codes. GETDATE(): Equivalent to Oracle’s TO_DATE In conclusion, GitHub Copilot streamlines code conversion by offering both quick suggestions and detailed explanations. The first part highlighted its ability to assist with code transitions, while the second part focused on how its explainability feature enhances the Oracle to Azure SQL migration, providing guided, efficient, and error-free conversion. This combination accelerates development and reduces potential issues during migration.GitHub Copilot and SSMA: Strap a GenAI conversion booster to your Oracle to SQL Migrations
In this blog, we’ll explore how GitHub Copilot and SQL Server Migration Assistant (SSMA) for Oracle can supercharge your code conversion journey from PL/SQL to T-SQL. Learn how both these tools bring comprehensive conversion rule set and Generative AI capabilities together to simplify and expedite your migration journey from Oracle to Azure SQL.
