1. Prepare Teradata Environment: Make sure you have the necessary permissions to read the data from the tables you want to export.

2. Select Data to Export: Identify the data you want to move to Databricks Lakehouse. It could be specific tables or the result of a query.

3. Export Data to a File:

   - Use the `BTEQ` (Basic Teradata Query) utility to export data. Write a BTEQ script that selects the data and writes it to a flat file (CSV or TSV).

   - Example BTEQ script to export data:

     ```sql

     .LOGON your_teradata_server/your_username,your_password;

     .EXPORT DATA FILE = /path/to/export/file.csv;

     .SET SEPARATOR ',';

     SELECT * FROM your_database.your_table;

     .EXPORT RESET;

     .LOGOFF;

     ```

   - Run the BTEQ script on the Teradata system.

4. Compress the Data: To optimize the transfer, compress the exported files using a tool like `gzip` or `zip`.

1. Choose a Cloud Storage Solution: Select a cloud storage provider (AWS S3, Azure Blob Storage, or Google Cloud Storage) that is compatible with Databricks.

2. Upload Data:

   - Use the cloud provider's CLI or web interface to upload the compressed data files to the chosen cloud storage.

   - Ensure the storage bucket or container is secured and has the correct permissions set up.

1. Set Up Databricks Environment:

   - Create a Databricks workspace if you don't already have one.

   - Start a Databricks cluster with the required configuration.

2. Mount Cloud Storage:

   - Mount the cloud storage bucket to Databricks using DBFS (Databricks File System) to make the data accessible from Databricks notebooks.

   - Use the following commands in a Databricks notebook to mount the storage:

     ```python

     dbutils.fs.mount(

       source = "s3a://your-bucket-name",

       mount_point = "/mnt/your-mount-name",

       extra_configs = {"fs.s3a.access.key": "your-access-key", "fs.s3a.secret.key": "your-secret-key"}

     )

     ```

3. Read Data into Databricks:

   - Use the `spark.read` function to read the data from the mounted cloud storage into a Spark DataFrame.

   - For example, to read a CSV file:

     ```python

     df = spark.read.csv("/mnt/your-mount-name/file.csv", header=True, inferSchema=True)

     ```

4. Transform Data (Optional):

   - Perform any necessary data transformations using Spark DataFrame operations.

5. Write Data to Databricks Lakehouse:

   - Use the `df.write` function to save the DataFrame to the Databricks Lakehouse in the desired format (Delta, Parquet, etc.).

   - For example, to write data as a Delta table:

     ```python

     df.write.format("delta").saveAsTable("your_table_name")

     ```

6. Verify Data:

   - Query the data using a Databricks notebook to ensure it was imported correctly.

1. Unmount Cloud Storage:

   - Once the data transfer is complete, unmount the cloud storage to prevent unauthorized access.

   - Use the following command in a Databricks notebook:

     ```python

     dbutils.fs.unmount("/mnt/your-mount-name")

     ```

2. Delete Temporary Files:

   - Remove any temporary files from Teradata and the cloud storage to maintain security and reduce costs.