Ensure that the required software and libraries are installed and configured. You'll need Python installed on your machine along with Pandas and PyArrow libraries to handle Parquet files. Additionally, ensure Oracle SQL*Loader is available for data loading into Oracle.

Use Python with Pandas and PyArrow to read the Parquet file. This will convert the Parquet data into a DataFrame, which makes it easier to manipulate and export into a format Oracle can understand.
```python
import pandas as pd
df = pd.read_parquet('yourfile.parquet')
```

Export the DataFrame to a CSV file. This intermediary step is crucial as it transforms the data into a format that can be easily processed by SQL*Loader.
```python
df.to_csv('data.csv', index=False)
```

Define the Oracle table structure to match the schema of the Parquet data. Use Oracle SQL Developer or command-line tools to execute the `CREATE TABLE` statement, ensuring data types and column names align with those in the Parquet file.

Create a control file for SQL*Loader that specifies how data should be loaded into the Oracle table. This file includes details such as the input CSV file name, table name, and data mapping.
```plaintext
LOAD DATA
INFILE 'data.csv'
INTO TABLE your_table_name
FIELDS TERMINATED BY ','
(column1, column2, column3, ...)
```

Run the SQL*Loader command to load the CSV data into the Oracle database. This step reads the control file and processes the CSV file to populate the Oracle table.
```shell
sqlldr userid=your_username/your_password@your_database control=your_control_file.ctl
```

After loading the data, verify the import process by querying the Oracle table. Use SQL queries to ensure the data is correctly populated and matches the source data from the Parquet file.
```sql
SELECT * FROM your_table_name WHERE ROWNUM <= 10;
```
By following these steps, you can successfully move data from a Parquet file to an Oracle database without relying on third-party connectors or integrations.