To begin, ensure you have API access to commercetools. Log into your commercetools account, navigate to the API Clients section, and create a new API client. Record the client ID, client secret, and project key. These credentials will allow you to authenticate and interact with the commercetools API.

Use the commercetools API to extract the data you need. You can do this by writing a script in a language like Python or Java. Utilize the commercetools API endpoints to request data. For example, to fetch product data, you would use the `/products` endpoint. Store the extracted data in a JSON or CSV format. Ensure you handle pagination and rate limits as per commercetools API documentation.

Once you have the data extracted, perform any necessary transformations to prepare it for transfer to the Databricks Lakehouse. This may include cleaning the data, converting it into a suitable format (e.g., CSV, Parquet), and ensuring it meets any schema requirements for your Lakehouse setup. Use tools like pandas in Python for efficient data manipulation.

Access your Databricks account and set up a new cluster if necessary. Ensure you have the appropriate permissions to access and write data to the Lakehouse. Configure your cluster’s libraries to include necessary packages for data import, such as `pyspark` for Spark functionality.

Use Databricks File System (DBFS) to transfer your data files. Utilize Databricks CLI or APIs to upload the prepared data files to DBFS. Ensure the files are uploaded to a directory where you have read and write permissions. This step acts as an intermediary storage before data is processed into the Lakehouse.

With your data in DBFS, create a Spark DataFrame to read each file into memory. Use Spark SQL or DataFrame API to load the data into your Lakehouse tables. Ensure you define the schema appropriately and map the data fields correctly. Store the data in a structured format, such as Delta Lake, to leverage ACID transactions and versioning.

After loading the data, verify the integrity and accuracy of the data in the Lakehouse. Perform checks to ensure all records are accounted for and data types are consistent. Optimize the data storage using Delta Lake features, such as partitioning and optimizing, to enhance query performance. Regularly schedule data refreshes as needed to keep the Lakehouse updated with the latest commercetools data.

By following these steps, you can successfully move data from commercetools to a Databricks Lakehouse without relying on third-party connectors or integrations.