1. Identify the Data to Export: Determine which collections you need to export from MongoDB.

2. Use `mongoexport`: Use the `mongoexport` utility to export the data from MongoDB to a JSON or CSV file. This tool is part of the MongoDB server installation.

   For JSON:

   ```sh

   mongoexport --db yourDatabaseName --collection yourCollectionName --out yourData.json

   ```

   For CSV (you'll need to specify the fields):

   ```sh

   mongoexport --db yourDatabaseName --collection yourCollectionName --type=csv --fields field1,field2 --out yourData.csv

   ```

1. Review Data Types: Check the MongoDB data types and map them to the corresponding Oracle data types.

2. Transform Data: If needed, write a script (in a language like Python, Perl, or even a shell script) to transform the data from the MongoDB export into a format suitable for Oracle. This may include:

   - Converting BSON ObjectId to a string.

   - Formatting dates to match Oracle's date format.

   - Handling nested documents or arrays appropriately.

3. Create an Intermediate File: Save the transformed data into an intermediate CSV or SQL file that can be understood by Oracle.

1. Create Tables: Create the necessary tables in Oracle Database to hold the data. Make sure the columns match the data types of the transformed data.

   ```sql

   CREATE TABLE your_table_name (

       column1 datatype1,

       column2 datatype2,

       ...

   );

   ```

2. Prepare the Environment: Ensure that the Oracle environment is ready to receive the data. This may include setting up user permissions, tablespaces, and other necessary database configurations.

1. Use SQL*Loader or External Tables: Decide whether to use SQL*Loader or Oracle External Tables for the import process.

   - SQL*Loader: This utility allows you to load data from external files into tables of an Oracle database.

     Create a control file that describes how the data is formatted and how it should be loaded into Oracle:

     ```sh

     LOAD DATA

     INFILE 'yourData.csv'

     INTO TABLE your_table_name

     FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"'

     (column1, column2, ...)

     ```

     Run SQL*Loader using the control file:

     ```sh

     sqlldr userid=yourOracleUsername/yourOraclePassword@yourOracleDB control=yourControlFile.ctl

     ```

   - External Tables: This feature allows you to query data from a flat file as though it is a regular table.

     ```sql

     CREATE TABLE your_table_name (

         column1 datatype1,

         column2 datatype2,

         ...

     )

     ORGANIZATION EXTERNAL (

         TYPE ORACLE_LOADER

         DEFAULT DIRECTORY your_directory

         ACCESS PARAMETERS (

             RECORDS DELIMITED BY NEWLINE

             FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"'

         )

         LOCATION ('yourData.csv')

     );

     ```

     After creating the external table, you can use `INSERT INTO ... SELECT FROM ...` to move the data into the actual table.

1. Check the Data: After the import process, verify that the data has been transferred correctly by running some queries on the Oracle tables.

   ```sql

   SELECT * FROM your_table_name;

   ```

2. Check for Errors: Review any logs or error files generated by SQL*Loader or the External Tables process to ensure that all records have been imported successfully.

1. Remove Temporary Files: After a successful import, remove any intermediate or temporary files that were created during the process.

2. Finalize the Database: Add any necessary indexes, constraints, or triggers to the Oracle tables to finalize the setup.

Notes:

- It's important to ensure that the character encoding is consistent between the exported data and the Oracle Database to avoid any issues with special characters.

- Always test the migration process with a subset of data before attempting to migrate the entire dataset.

- Back up your databases before beginning the migration process to prevent data loss in case of any issues.

- Since you are not using third-party connectors or integrations, you'll need to handle data type conversions and complex data structures manually, which can be error-prone and time-consuming. Ensure thorough testing and validation during each step.