How to load data from SurveySparrow to Databricks Lakehouse

Learn how to use Airbyte to synchronize your SurveySparrow data into Databricks Lakehouse within minutes.

Try Airbyte Cloud Manual method (the hard way)

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a SurveySparrow connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted SurveySparrow data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the SurveySparrow to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync to Manually

Step 1: Export Survey Data from SurveySparrow

Begin by logging into your SurveySparrow account. Navigate to the survey dashboard where you can view your collected responses. Use the export feature to download the survey data as a CSV file. Ensure that you have the necessary permissions to export data and that you save the file in a secure, accessible location on your local machine.

Step 2: Prepare the CSV File for Import

Open the CSV file using a spreadsheet software like Microsoft Excel or Google Sheets. Check the data format and clean any discrepancies such as missing values or incorrect data types. Make any necessary adjustments to ensure consistency and accuracy. Save the cleaned data as a CSV file again.

Step 3: Set Up Databricks Lakehouse Environment

Log into your Databricks account and create a new workspace if you do not already have one. Ensure that your environment is set up with the necessary compute resources. You may need to configure a cluster that can handle the data import and processing tasks.

Step 4: Transfer CSV File to Databricks File System (DBFS)

Access the Databricks workspace and navigate to the “Data”� section. Utilize the file upload feature to transfer the CSV file from your local machine to the Databricks File System (DBFS). This step involves using the Databricks UI to locate and upload your file, ensuring it is stored in a directory accessible for further processing.

Step 5: Create a Delta Table in Databricks

In the Databricks notebook, write the necessary Spark SQL commands to create a Delta table. Use the DataFrame API to read the CSV file from DBFS and define the schema based on the structure of your survey data. Execute the commands to create a Delta table where the survey data will be stored.

Step 6: Load Data into Delta Table

Utilize PySpark or Scala within the Databricks notebook to load the data from the CSV file into the Delta table. This involves reading the CSV data into a DataFrame and using the `write` method to insert the data into the Delta table. Ensure that you handle any data conversion or type casting as needed to match the Delta table schema.

Step 7: Verify Data Transfer and Clean Up

Once the data is loaded, perform a series of queries on the Delta table to verify that the data has been transferred correctly. Check for data integrity and completeness. After verification, clean up any temporary files in DBFS to maintain a tidy workspace. Document the process for future reference or replication.