1. Connect to Redshift Cluster:

   - Use a SQL client to connect to your Redshift cluster. You can use the `psql` command-line tool or any SQL client that supports Redshift.

2. Run Unload Command:

   - Use the `UNLOAD` command to export the data from Redshift to Amazon S3 as flat files (CSV format is common). For example:

     ```sql

     UNLOAD ('SELECT * FROM your_redshift_table')

     TO 's3://yourbucket/yourdata/'

     IAM_ROLE 'arn:aws:iam::0123456789012:role/YourRedshiftRole'

     CSV

     DELIMITER ','

     ALLOWOVERWRITE;

     ```

3. Download Files from S3:

   - Download the flat files from the S3 bucket to your local machine or directly to the server where Oracle Database is hosted. You can use the AWS CLI for this purpose:

     ```

     aws s3 cp s3://yourbucket/yourdata/ /path/to/local/directory --recursive

     ```

1. Connect to Oracle Database:

   - Use SQL*Plus or any other Oracle client to connect to your Oracle Database.

2. Create a Table:

   - Create a table in Oracle with the same structure as the Redshift table you exported. For example:

     ```sql

     CREATE TABLE your_oracle_table (

       column1 datatype,

       column2 datatype,

       ...

     );

     ```

3. Prepare Directory Object:

   - Create a directory object in Oracle that points to the directory where you will place the flat files on the Oracle server.

     ```sql

     CREATE DIRECTORY data_dir AS '/path/to/local/directory';

     ```

1. Place Files on Oracle Server:

   - If you haven't already, transfer the flat files from your local machine to the Oracle server in the directory corresponding to the directory object created in the previous step.

2. Grant Permissions:

   - Make sure that the Oracle user has the necessary permissions to read from the directory.

     ```sql

     GRANT READ ON DIRECTORY data_dir TO your_oracle_user;

     ```

3. Run Import Command:

   - Use SQL*Loader, Oracle Data Pump, or external tables to import the data from the flat files into the Oracle table. For SQL*Loader, you would use a control file to specify the format of the data. An example control file (`load_data.ctl`) might look like this:

     ```

     LOAD DATA

     INFILE '/path/to/local/directory/yourdata000'

     INTO TABLE your_oracle_table

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

     (column1, column2, ...)

     ```

   - Then run the SQL*Loader command:

     ```

     sqlldr your_oracle_user/password@your_oracle_db control=load_data.ctl

     ```

1. Check the Import:

   - After the import process is complete, run some queries to ensure that the data has been imported correctly and completely.

2. Verify Row Counts:

   - Compare the row counts between the source table in Redshift and the target table in Oracle.

3. Perform Data Quality Checks:

   - Run some data quality checks to make sure that the data types have been preserved and there are no issues with the data.

1. Remove Temporary Files:

   - Once you have verified the data integrity, you can remove the flat files from the Oracle server and the S3 bucket to free up space.

2. Drop Directory Object:

   - If the directory object is no longer needed, you can drop it from Oracle to maintain security:

     ```sql

     DROP DIRECTORY data_dir;

     ```

Additional Considerations

- Security: Ensure that the data transfer is secure, especially if it contains sensitive information. Use encryption where possible.

- Data Types: Be careful with data type compatibility between Redshift and Oracle. You may need to perform data type conversions during the export or import process.

- Performance: For large datasets, consider parallel export and import to improve performance.

- Automation: Script the process to make it repeatable and less prone to human error.

This guide provides a high-level overview of the steps required to move data from Redshift to Oracle without third-party connectors. Depending on the specific requirements and the size of the data, some steps may need to be adjusted for optimal performance and security.