A. Use Salesforce Reports or Data Export Service

1. Log in to your Salesforce account.
2. Navigate to the “Reports” tab and create a report that includes all the data you want to transfer.
3. Run the report and export the data to a CSV file.
4. If you need to export large amounts of data or the entire database, consider using the Salesforce Data Export Service, which can be scheduled or run immediately.

B. Use Salesforce Data Loader for Custom Queries

1. Install Salesforce Data Loader on your local machine.
2. Log in to Data Loader using your Salesforce credentials.
3. Select “Export” or “Export All” for including soft-deleted records.
4. Write a SOQL query to specify the data you want to extract.
5. Export the data to a CSV file.

1. Review the CSV files and ensure that the data is clean and formatted correctly for import into Databricks.
2. Remove any unnecessary columns or rows that are not needed in Databricks.
3. If needed, split large CSV files into smaller chunks to facilitate easier uploading and processing.

1. Log in to your Databricks workspace.
2. Create a new cluster or start an existing cluster that you will use for data import.
3. Install any necessary libraries that you might need for data processing.

1. In Databricks, navigate to the “Data” tab.
2. Click on “Add Data” and then choose “DBFS” to upload your files directly to DBFS using the Databricks UI.
3. Alternatively, use the Databricks CLI to upload your CSV files to DBFS.

databricks fs cp <local_file_path.csv> dbfs:/<databricks_file_path.csv>

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1. Process and Transform Data (Optional)In Databricks, create a new notebook.
2. Use the following code snippet to load the CSV data into a DataFrame:

file_location = "/<databricks_file_path.csv>"

file_type = "csv"

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# CSV options

infer_schema = "true"

first_row_is_header = "true"

delimiter = ","

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# Load the data into a DataFrame

df = spark.read.format(file_type) \

  .option("inferSchema", infer_schema) \

  .option("header", first_row_is_header) \

  .option("sep", delimiter) \

  .load(file_location)

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# Show the DataFrame

df.show()

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1. Perform any transformations or processing required on the DataFrame within Databricks.
2. Use Spark SQL or DataFrame API to manipulate the data as needed.

1. Decide on the storage format (e.g., Delta Lake, Parquet) and the location where you want to store the data within Databricks.
2. Use the DataFrame API to write the data to the Lakehouse.

data_location = "/mnt/<desired_path_in_databricks>"

df.write.format("delta").save(data_location)

1. After the data has been loaded, run queries against the data to ensure it has been transferred correctly.
2. Check for any discrepancies or data loss during the transfer process.

1. To automate this process, you can create a job in Databricks that regularly runs a notebook or a script that performs these steps.
2. Schedule the job according to your data refresh requirements.