First, ensure DuckDB is installed on your system. You can install it via Python by running `pip install duckdb` in your terminal or command prompt. For standalone binaries, visit the DuckDB [official website](https://duckdb.org) and follow the installation instructions for your operating system.

Ensure that your data is in a common format that DuckDB supports, such as CSV, Parquet, or JSON. Format the data appropriately, ensuring consistent column names and data types across your dataset to avoid issues during import.

You can interact with DuckDB through its shell or via a Python script. To use the shell, open a terminal and type `duckdb` to start. If using Python, import DuckDB by adding `import duckdb` at the start of your script.

If you’re not connecting to an existing database, create a new one. In the DuckDB shell, type `CREATE DATABASE my_database;` and `USE my_database;`. In Python, initiate a connection with `con = duckdb.connect('my_database.db')`.

Define the structure of the table you want to populate with your data. Use the `CREATE TABLE` statement to specify the table name, column names, and data types. For example:
```sql
CREATE TABLE my_table (
id INTEGER,
name VARCHAR,
age INTEGER
);
```
Execute this SQL command in the DuckDB shell or through a Python script using `con.execute('CREATE TABLE ...')`.

Use the `COPY` command to load data from a file into the DuckDB table. For a CSV file, the command is:
```sql
COPY my_table FROM 'path/to/your/data.csv' (DELIMITER ',', HEADER);
```
Execute this command in the DuckDB shell or via Python with `con.execute('COPY ...')`. Adjust the file path and options according to your data format and structure.

After loading the data, verify the import by running a simple query like `SELECT * FROM my_table LIMIT 10;` to view a subset of the data. This step ensures that the data has been imported correctly and is available for querying.
By following these steps, you can successfully move data into DuckDB without relying on any third-party connectors or integrations.