How to load data from Typeform to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Typeform data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from Typeform
Begin by logging into your Typeform account. Navigate to the desired form and select the "Results" tab. From there, choose the "Export" option. Export your data in a CSV format, as it is widely compatible and easy to handle. Save the CSV file to a secure location on your local machine.
Step 2: Set Up Databricks Workspace
Access your Databricks account and set up a new workspace if you haven't done so already. Ensure you have the necessary permissions and resources to create clusters and upload data. Familiarize yourself with the Databricks user interface and the fundamentals of the Lakehouse architecture.
Step 3: Upload CSV to Databricks File System (DBFS)
Within your Databricks workspace, navigate to the "Data" tab. Click on "Add Data" and choose the option to upload files. Select the CSV file you exported from Typeform and upload it to the Databricks File System (DBFS). This step makes the data accessible for further processing within Databricks.
Step 4: Create a New Databricks Notebook
Go to the "Workspace" tab and create a new notebook. This notebook will be used to process and import your data into the Lakehouse. Choose a preferred language (Python, Scala, R, SQL) for your notebook based on your familiarity and the requirements of your data processing tasks.
Step 5: Read CSV Data into a DataFrame
Use the appropriate code in your notebook to read the CSV file from DBFS into a DataFrame. For example, if using Python with PySpark, you can use:
```python
df = spark.read.csv('/FileStore/path_to_your_file.csv', header=True, inferSchema=True)
```
This command imports the CSV data into a DataFrame, allowing you to perform further transformations and analyses.
Step 6: Transform Data as Needed
Utilize Spark DataFrame operations to clean and transform your data as required. This may include filtering rows, renaming columns, handling missing values, or converting data types. Ensure that the data is in the desired format and structure for analysis or storage in the Lakehouse.
Step 7: Save DataFrame to Databricks Lakehouse
Finally, save the processed DataFrame to your Databricks Lakehouse. You may write it to a Delta table for efficient querying and storage. For example:
```python
df.write.format('delta').save('/mnt/your_lakehouse_path/your_table_name')
```
This command writes the DataFrame to your specified Lakehouse path, effectively integrating the Typeform data into your Databricks environment for future use and analysis.
By following these steps, you can manually move data from Typeform to Databricks Lakehouse without relying on third-party connectors or integrations.