At Google Cloud Next ‘23, we announced support in Database Migration Service (DMS) for Oracle-to-PostgreSQL migrations to make them smooth, intuitive and efficient. We also introduced integrated code and schema conversions to reduce friction in the most time-consuming phases of the migration.
This week at Google Cloud Next ‘24, we unveiled Gemini for Google Cloud, a new generation of AI-assistive capabilities based on Google’s Gemini family of models, including Gemini in Databases.
In addition, we announced the preview of a key feature of Gemini in Database: assistive code and schema conversion, allowing you to speed up the modernization of Oracle databases to PostgreSQL databases on Google Cloud. Finally, we announced Gemini-assisted code explainability, a key feature of Gemini in Databases, which simplifies code conversion and helps developers become proficient with the PostgreSQL dialect. Let’s dive in.
Gemini-assisted code and schema conversion
Migrating code and schema from Oracle PL/SQL to PostgreSQL can be very challenging due to to the inherent differences between them in terms of syntax, data types, and procedural constructs, etc. The distinct nature of built-in functions, transaction-handling mechanisms, and system-specific objects further complicates the conversion process. When migrating data, developers must meticulously address variations in error handling, security models, and procedural language specifics.
Yet successfully navigating these disparities is essential if you want to adopt Cloud SQL or AlloyDB for PostgreSQL. And while automatic code conversion can handle the bulk of the migration, there’s often a last mile that requires manual intervention and user feedback. In Database Migration Service, Gemini is trained on your manual interventions, learning based on the edits you make, your responses to its real-time recommendations about the intricacies of your specific code patterns and transformations, and from other commonalities across your code base.
For example, consider Oracle Bulk Collect, which lacks a direct equivalent in PostgreSQL. In the DMS conversion workspace, adjusting SQL code and converting Bulk Collect into PostgreSQL “For,” “In”, and “Loop” statements is a manual process. But once the code is changed manually, Gemini identifies other occurrences so it can suggest additional edits based on what it learned.
