Begin by exporting the data you need from Pardot. Log into your Pardot account and navigate to the 'Reports' section. Choose the dataset you wish to export, such as leads or prospects. Use the 'Export' function to download the data in CSV format, which is a common and easily manageable file type for data transfer purposes.

Once you have your CSV file, open it using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure it is accurate and clean. Remove any unnecessary columns or data that you do not wish to transfer to Weaviate. Ensure that the data structure matches the schema you plan to use in Weaviate, including field names and data types.

Set up Weaviate in your environment. You can do this by running Weaviate locally using Docker. To do this, ensure Docker is installed on your machine, then run the Weaviate Docker container using the following command:
```
docker run -d --name weaviate -p 8080:8080 semitechnologies/weaviate:latest
```
This will start a Weaviate instance on your local machine, making it ready to accept data.

Access the Weaviate console (typically accessible at http://localhost:8080 if running locally) to define the schema that matches the structure of your Pardot data. Use the Weaviate schema API or console to define classes and properties that correspond to the columns in your CSV file. A schema in Weaviate acts as a blueprint for how data is structured and queried.

Convert your cleaned CSV data into JSON format, as Weaviate uses JSON for data ingestion. You can use a script in Python or another language to automate this process. Here's a basic example using Python:
```python
import csv
import json

csv_file_path = 'your-data.csv'
json_file_path = 'your-data.json'

with open(csv_file_path, mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
data = [row for row in csv_reader]

with open(json_file_path, mode='w') as json_file:
json.dump(data, json_file)
```
This script reads the CSV file and outputs a JSON file that you can use to import data into Weaviate.

Use the Weaviate REST API to import the JSON data. You can write a script to automate this process. Here’s an example using Python and the `requests` library:
```python
import requests
import json

url = 'http://localhost:8080/v1/objects'
headers = {'Content-Type': 'application/json'}

with open('your-data.json') as json_file:
data = json.load(json_file)

for item in data:
response = requests.post(url, headers=headers, json=item)
if response.status_code != 200:
print(f"Failed to import item: {item}, Error: {response.text}")
```
This script will post each JSON object to the Weaviate instance, adding it to your database.

After importing the data, verify that it has been correctly ingested by querying your Weaviate instance. Use the Weaviate console or API to perform queries and ensure the data structure aligns with your schema. This step ensures that all data fields are correctly mapped and that the data can be accessed as expected.

By following these steps, you can successfully transfer data from Pardot to Weaviate without relying on third-party connectors or integrations.