How to load data from Bing Ads to Databricks Lakehouse

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Bing Ads 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 Bing Ads 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 Bing Ads 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 Bing Ads

Start by exporting the data you need from Bing Ads. Log in to your Bing Ads account and navigate to the Reports section. Create a custom report with the required metrics and dimensions. Once the report is generated, export it in a CSV format to ensure compatibility with further processes.

Set up a secure, intermediate storage location where you can temporarily store the exported CSV files. This could be a secure FTP server or a cloud-based storage solution like Azure Blob Storage or AWS S3, depending on your available resources and compliance requirements.

Use secure methods like SCP, SFTP, or cloud storage CLI tools to transfer the exported CSV files from your local machine to the secure transfer location. Ensure that the transfer method is encrypted to protect sensitive ad data during transit.

Log into your Databricks account and set up the necessary environment. Create a new Databricks notebook and ensure that your workspace has access to the secure transfer location. Configure any necessary permissions to allow Databricks to read from this location.

Use Databricks utilities to read the CSV files from the secure location into a Spark DataFrame. For example, if using Azure Blob Storage, you can use the `spark.read.format("csv")` function along with the appropriate storage account credentials and file path.

Transform the data within Databricks as needed. This can include data cleaning, type conversion, and any necessary computations or aggregations. Utilize Spark SQL or PySpark to perform these operations efficiently on the DataFrames.

Finally, load the transformed data into the Databricks Lakehouse. You can write the processed DataFrame to your Lakehouse storage layer using the `DataFrame.write` method, specifying the desired format (e.g., Delta, Parquet) and location within your Lakehouse architecture.

By following these steps, you can successfully move data from Bing Ads to a Databricks Lakehouse without relying on third-party connectors or integrations.