How to load data from Workable to Databricks Lakehouse

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Workable 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 Workable 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 Workable 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 Data from Workable

First, log in to your Workable account and navigate to the data or reports section. Use the built-in export functionality to download your desired data as a CSV or Excel file. This file will serve as the base data for migration to Databricks Lakehouse.

Ensure that you have Python and any necessary libraries installed on your local machine. Python is commonly used for data manipulation tasks, and you'll likely need libraries such as pandas for handling your CSV or Excel files. Install any required packages using pip, e.g., `pip install pandas`.

Load the exported data file into a pandas DataFrame. Perform any necessary data cleaning or transformations locally. This might include handling missing values, renaming columns, or converting data types to match the schema you plan to use in Databricks Lakehouse.

Access your Databricks Lakehouse environment through your Databricks account. Create a new cluster if necessary, ensuring it is running and ready to accept data. Set up any required configurations for data import, such as defining the expected schema and creating any necessary database tables.

Use the Databricks web interface to upload your cleaned data file to the Databricks File System. Navigate to the "Data" tab, select "Add Data," and choose the option to upload your file directly from your local system to DBFS.

Once your data file is on DBFS, use a Databricks notebook to load the data into a DataFrame. Utilize Spark’s `read` functions to import the data from the CSV or Excel file stored in DBFS. For example, use `spark.read.csv("/dbfs/path/to/your/file.csv")` to read the data into a Spark DataFrame.

Verify the integrity and accuracy of the imported data by performing exploratory data analysis in a Databricks notebook. Once verified, write the DataFrame to a table in your Databricks Lakehouse using the `write` method. For instance, use `dataframe.write.format("delta").saveAsTable("your_table_name")` to save your data in the Delta Lake format, ensuring it is stored and accessible for analytics and further processing.

By following these steps, you can manually transfer data from Workable to your Databricks Lakehouse environment, ensuring clean and accurate data migration without relying on third-party tools.