Start by thoroughly reviewing the Everhour API documentation to understand the available endpoints, authentication methods, and data structures. This is crucial for extracting the necessary data effectively.

Create a script or program (using a language such as Python, Node.js, etc.) to authenticate and interact with the Everhour API. Use the provided authentication method, usually API keys, to access the data. Ensure you can successfully make requests to the API and receive responses.

Determine which data from Everhour you need to transfer to Elasticsearch. Use your API client to extract this data by making appropriate API calls. Handle pagination if necessary, to ensure you retrieve all relevant data.

Convert the data obtained from Everhour into a format suitable for Elasticsearch. This typically involves transforming the data into JSON format, which Elasticsearch can index. Pay attention to data types and structures to ensure compatibility.

Prepare your Elasticsearch environment by creating an index where the data will be stored. Define the mapping for your index, specifying the fields and their data types, to ensure the data is indexed correctly.

Develop a script to send the transformed data to Elasticsearch using its REST API. Use the bulk API if you are dealing with large volumes of data, to optimize performance. Ensure each document is indexed correctly and verify the success of the operation by checking Elasticsearch's response.

Once your data transfer process is functioning correctly, automate it to run on a regular schedule. Use cron jobs (on Unix-based systems) or Task Scheduler (on Windows) to execute your script at desired intervals. This will ensure that your Elasticsearch database remains up-to-date with the latest data from Everhour.

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