To access Twitter's data, you need to have a Twitter Developer Account. Sign up at the Twitter Developer Portal, create a new project, and obtain your API keys and tokens. These include the API Key, API Secret Key, Access Token, and Access Token Secret. These credentials are essential for making authenticated requests to Twitter's API.

Ensure you have Python installed on your system. Use pip to install the necessary Python packages: `tweepy` for interacting with Twitter's API and `duckdb` for database operations. Run the following command in your terminal or command prompt:
```bash
pip install tweepy duckdb
```

Write a Python script to connect to Twitter's API and fetch the data you need. Use the `tweepy` library to authenticate with your credentials and access Twitter data. Define the parameters of the data you are interested in, such as specific user timelines, hashtags, or keywords.

Example code snippet to authenticate and fetch tweets:
```python
import tweepy

# Replace with your credentials
API_KEY = 'your_api_key'
API_SECRET_KEY = 'your_api_secret_key'
ACCESS_TOKEN = 'your_access_token'
ACCESS_TOKEN_SECRET = 'your_access_token_secret'

# Authenticate to Twitter
auth = tweepy.OAuthHandler(API_KEY, API_SECRET_KEY)
auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)

# Create an API object
api = tweepy.API(auth)

# Fetch tweets
tweets = api.user_timeline(screen_name='twitter_handle', count=100)
```

Once you have fetched the tweets, process and structure the data into a suitable format for storage in DuckDB. Convert the tweet objects into a list of dictionaries or a pandas DataFrame, focusing on relevant fields like `created_at`, `text`, `user`, etc.

Example using pandas:
```python
import pandas as pd

tweet_data = []
for tweet in tweets:
tweet_data.append({
'created_at': tweet.created_at,
'text': tweet.text,
'user': tweet.user.screen_name,
'retweet_count': tweet.retweet_count,
'favorite_count': tweet.favorite_count
})

df = pd.DataFrame(tweet_data)
```

Initialize a DuckDB database where the data will be stored. You can create an in-memory database or a persistent one by specifying a file name.

Example:
```python
import duckdb

# Create or connect to a DuckDB database
conn = duckdb.connect('twitter_data.duckdb')
```

Use DuckDB's Python API to insert the structured data into your database. If you have it as a pandas DataFrame, you can directly write it to DuckDB.

Example:
```python
# Insert the DataFrame into DuckDB
conn.execute('CREATE TABLE IF NOT EXISTS tweets AS SELECT * FROM df')
```

After inserting the data, query the DuckDB database to ensure that the data has been correctly stored. Use SQL queries to retrieve and verify the data.

Example:
```python
result = conn.execute('SELECT * FROM tweets LIMIT 5').fetchall()
print(result)
```

By following these steps, you seamlessly transfer data from Twitter into DuckDB without relying on third-party connectors or integrations, maintaining control over the data handling process.