Before you begin, familiarize yourself with the SpaceX API documentation to know what data you can access (e.g., launches, rockets, payloads). Similarly, understand Weaviate’s schema and data model. Weaviate is a vector search engine that uses a schema-based approach, so knowing its requirements is crucial for data import.

Install necessary tools and dependencies. You'll need Python (or another programming language of your choice) and libraries such as `requests` for making HTTP requests, and `weaviate-client` to interact with Weaviate. Ensure your environment is configured to access both SpaceX API and your Weaviate instance.

Write a script to send HTTP GET requests to the SpaceX API endpoints. Use the `requests` library to make these calls. For example, to fetch launch data, you might use:
```python
import requests

response = requests.get('https://api.spacexdata.com/v4/launches')
spacex_data = response.json()
```
Ensure you handle pagination and rate limits as specified by the API documentation.

Convert the fetched data into a format that aligns with your Weaviate schema. This might involve renaming keys, changing data types, or restructuring nested data. Create a function to iterate over the SpaceX data and transform it:
```python
def transform_data(spacex_data):
transformed_data = []
for item in spacex_data:
transformed_data.append({
'name': item['name'],
'date_utc': item['date_utc'],
'rocket': item['rocket'],
'details': item['details'],
# Add more fields as required
})
return transformed_data
```

Define your Weaviate schema to match the structure of the transformed SpaceX data. Use the Weaviate client to create classes and properties that reflect the data you plan to import. This step ensures that Weaviate knows how to store and index the incoming data.

Use the Weaviate client library to push the transformed data into your Weaviate instance. For each object, use the `client.data_object.create()` method to insert the data:
```python
import weaviate

client = weaviate.Client("http://localhost:8080")

for item in transformed_data:
client.data_object.create(
data_object=item,
class_name='Launch'
)
```
Ensure that your Weaviate instance is running and accessible.

After importing the data, perform queries to verify that it has been correctly stored in Weaviate. Use Weaviate’s query language to check that all fields are populated correctly and that the data is searchable. This step is crucial to ensure that the data transfer was successful and that there are no discrepancies.

By following these steps, you can effectively move data from the SpaceX API to Weaviate without using third-party connectors or integrations.