Start by exporting the data you need from Waiteraid. This can typically be done through Waiteraid's user interface or command line tools. Look for an export option that allows you to download data in a common format like CSV, JSON, or Parquet. Ensure you understand the structure and contents of the data you are exporting.

After exporting, organize the data files on your local machine. Ensure the files are properly named and stored in a directory that is easily accessible. Check the data for any inconsistencies or errors that might have occurred during export. If necessary, clean the data to ensure it is in a suitable format for importing into DuckDB.

If you haven’t already installed DuckDB, download and install it on your local machine. DuckDB is available as a standalone binary, or you can install it via package managers like pip for Python. Ensure you have the latest stable version to take advantage of the newest features and bug fixes.

Open a terminal or command line, navigate to the directory where you want to store your DuckDB database, and create a new database file by entering:
```
duckdb my_database.duckdb
```
This command initializes a new DuckDB database file named `my_database.duckdb`.

Use DuckDB's SQL interface to load your data. Start DuckDB by running:
```
duckdb my_database.duckdb
```
Then, use the `COPY` command to import your data files. For example, if you have a CSV file, use:
```sql
COPY my_table FROM 'path/to/your/file.csv' (AUTO_DETECT TRUE);
```
Replace `my_table` with the name you want for the table in DuckDB and `path/to/your/file.csv` with the actual path to your CSV file.

After importing the data, verify that it has been loaded correctly into DuckDB. Run SQL queries to inspect the data, such as:
```sql
SELECT * FROM my_table LIMIT 10;
```
Check for any discrepancies or errors in the data compared to the original dataset in Waiteraid.

To enhance query performance, consider creating indexes on columns that you frequently query. DuckDB supports various optimization techniques, so explore options like creating indexes or partitioning large tables. Use SQL commands such as:
```sql
CREATE INDEX ON my_table(column_name);
```
Replace `column_name` with the name of the column you wish to index. This step will ensure that your database is set up for efficient querying and analysis.