To extract data from FullStory, familiarize yourself with its REST API. Review the API documentation to understand the available endpoints, authentication methods, and data formats. Typically, you'll need an API key to authenticate your requests.

Prepare a local or cloud-based environment to run scripts. Install necessary tools such as Python or Node.js, along with libraries for making HTTP requests (e.g., `requests` for Python or `axios` for Node.js) and handling JSON data.

Write a script to make API calls to FullStory and retrieve the desired data. Use your API key to authenticate requests. Depending on your needs, you might gather data like user sessions, events, or other analytics. Ensure your script handles pagination if FullStory returns data in multiple pages.

Transform the extracted data into a format compatible with Elasticsearch, typically JSON. Consider any necessary data cleaning or restructuring, such as flattening nested structures or converting timestamps into the required format.

Install and configure Elasticsearch on your server or use a managed service like Elastic Cloud. Ensure your Elasticsearch instance is accessible and you have the necessary permissions to create indices and insert data.

Write a script to send the transformed data to Elasticsearch. Use the Elasticsearch REST API to create indices and bulk insert documents. Handle any potential errors, such as data conflicts or connectivity issues, by implementing proper error-checking and retry logic.

After loading the data, verify that it appears correctly in Elasticsearch by querying the indices. Set up monitoring to ensure ongoing data integrity and performance. This could involve creating dashboards using Kibana or writing custom scripts to check for discrepancies or failures.

By following these steps, you can efficiently move data from FullStory to Elasticsearch without relying on third-party connectors or integrations.