How to load data from LinkedIn Ads to Databricks Lakehouse
Learn how to use Airbyte to synchronize your LinkedIn Ads data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Access LinkedIn Ads Data
Begin by accessing your LinkedIn Ads account. Navigate to the Campaign Manager and identify the campaigns or datasets you need to export. LinkedIn allows you to download reports in CSV format directly from the Campaign Manager. Ensure you have the necessary permissions to export this data.
Step 2: Export Data from LinkedIn Ads
Once you have identified the data you need, use LinkedIn's built-in export functionality to download the data as a CSV file. You can typically find this option under the "Export" button in the Campaign Manager. Choose the appropriate metrics and date range for your analysis before exporting.
Step 3: Prepare the Data for Upload
After exporting the data, review the CSV file to ensure that it includes all necessary fields and is formatted correctly. Clean the data as needed by removing duplicates, handling missing values, and converting data types for compatibility with Databricks Lakehouse. Save the cleaned data locally on your machine.
Step 4: Set Up Databricks Lakehouse Environment
Log into your Databricks account and set up a new workspace if one is not already available. Create a new cluster or use an existing one, ensuring that it has adequate resources to handle the data you plan to upload. Configure the cluster settings as per your requirements.
Step 5: Upload Data to Databricks
Once your environment is set up, navigate to the "Data" tab in Databricks and select "Add Data." Here, you'll be able to upload your CSV file. Follow the prompts to choose your file from your local system and specify any necessary options, such as delimiter type and whether to infer schema automatically.
Step 6: Transform and Store Data in Lakehouse
After uploading, use Spark SQL or Databricks notebooks to transform the data as needed. You might need to perform operations such as normalizing data formats, joining datasets, or aggregating metrics. Once the data is transformed, save it to a suitable format (e.g., Delta Lake format) in the Databricks Lakehouse for optimized querying and storage.
Step 7: Validate and Automate Data Updates
Validate the data in the Databricks Lakehouse by running sample queries to ensure accuracy. Once verified, consider setting up a script or notebook in Databricks to automate this data import process in the future. You can use Databricks Jobs or a scheduling tool to run this script at regular intervals, ensuring your data remains up-to-date.
By following these steps, you can effectively move data from LinkedIn Ads to a Databricks Lakehouse without relying on third-party connectors or integrations.