Start by thoroughly examining the data structure in your Convex development environment. Identify the data types, relationships, and schema details. This understanding is crucial for mapping data correctly to Postgres.

If you haven’t already, set up a Postgres database on your local machine or a server. Install PostgreSQL and create the necessary database and tables that will receive the data from Convex. Ensure the table schemas in Postgres are compatible with the data structure in Convex.

Use the Convex API or CLI tools to export data to a local file. This could be in JSON, CSV, or any other format that can be easily processed. Ensure that you have the necessary permissions and access rights to perform the export from Convex.

Depending on the format of the exported data, use a script or tool (like Python or Node.js) to transform the data into a format that matches the Postgres table schema. This may involve data type conversions and restructuring nested data.

Write SQL scripts to import the transformed data into Postgres. This involves creating `COPY` commands for CSV data or `INSERT` statements if dealing with JSON. Make sure these scripts handle any potential data inconsistencies, such as handling null values or default values.

Execute the prepared SQL scripts to load the data into your Postgres database. You can use the `psql` command-line tool to run these scripts. Monitor the process to catch and resolve any errors, such as data type mismatches or constraint violations.

After importing the data, perform thorough checks to ensure data integrity and consistency. Compare sample records between Convex and Postgres to verify accuracy. Run queries to ensure all data has been transferred and that the relationships and constraints are correctly implemented in the Postgres database.

By following these steps, you can efficiently migrate data from a Convex development environment to a Postgres database without the need for third-party connectors or integrations.