Before you begin, familiarize yourself with Weaviate’s data model and schema requirements. Weaviate organizes data into classes, each with properties. Ensure that your JSON data can be mapped to a Weaviate schema, keeping in mind data types and relationships.

Install Weaviate locally or set up a Weaviate instance on a server. Ensure that your Weaviate instance is running and accessible. You can use Docker to run Weaviate locally with a simple command:
```bash
docker run -d -p 8080:8080 semitechnologies/weaviate
```
Check that you can access the instance by opening `http://localhost:8080/v1` in your web browser.

Using Weaviate’s RESTful API, define the schema that corresponds to the structure of your JSON data. This involves creating classes and properties that will hold the data. You can use the following cURL command as an example to create a class:
```bash
curl -X POST "http://localhost:8080/v1/schema" \
-H "Content-Type: application/json" \
-d '{
"classes": [{
"class": "YourClassName",
"properties": [
{
"name": "propertyName",
"dataType": ["string"]
}
]
}]
}'
```

Write a script in Python (or another language you prefer) to parse your JSON file. Ensure the script reads the JSON data and prepares it for insertion into Weaviate. Use Python’s built-in JSON library to load the file:
```python
import json
with open('data.json') as f:
data = json.load(f)
```

Transform your JSON data into a format that matches your Weaviate schema. Create a list of objects where each object corresponds to an instance of a class in Weaviate. For example:
```python
weaviate_objects = []
for item in data:
weaviate_objects.append({
'class': 'YourClassName',
'properties': {
'propertyName': item['jsonKey']
}
})
```

Use Weaviate’s REST API to insert data. You will need to send POST requests with your data objects to the `/v1/objects` endpoint. Here's an example using Python’s `requests` library:
```python
import requests
url = "http://localhost:8080/v1/objects"
headers = {
'Content-Type': 'application/json'
}
for obj in weaviate_objects:
response = requests.post(url, headers=headers, json=obj)
if response.status_code != 200:
print(f"Failed to insert object: {response.content}")
```

Finally, verify that your data was correctly inserted into Weaviate. You can use the API to query the data and ensure it matches the expected structure. A simple GET request can confirm the presence of your data:
```bash
curl -X GET "http://localhost:8080/v1/objects"
```
By following these steps, you can effectively move data from a JSON file into Weaviate without using third-party connectors or integrations.