How to load data from Pinterest to Databricks Lakehouse

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

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Set up a Pinterest 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 Pinterest 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 Pinterest 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: Extract Data from Pinterest Ads

Start by manually exporting the necessary data from Pinterest Ads. Log into your Pinterest Ads Manager account, navigate to the reporting section, and select the desired campaign, ad group, or ad level data. Choose the appropriate date range and metrics you need. Use the export function to download the data as a CSV file, which will serve as your raw data source.

Ensure you have a suitable environment to work with your data. Install essential software such as Python and any necessary libraries (like pandas) if you're going to process the data programmatically. Confirm that you have access to the Databricks Lakehouse and can authenticate to it.

Before uploading the data to Databricks, clean and transform it as needed. Use Python's pandas library to read the CSV file, handle missing values, and format the data properly. This step ensures that the data adheres to the schema and quality standards required by your Databricks Lakehouse.

Access your Databricks account and create a new cluster or use an existing one. Make sure you have the necessary permissions to upload and manage data in the Lakehouse. Set up your workspace to handle the data you're about to import.

Use Databricks' web interface or the Databricks CLI to upload your cleaned CSV file to the Databricks File System (DBFS). This step involves transferring the CSV file from your local machine to the DBFS so that it can be accessed by your Databricks notebooks and jobs.

Use a Databricks notebook to read the CSV file from DBFS and load it into a Delta table within your Lakehouse. Utilize Spark DataFrames to read the CSV file and perform any final transformations required. Write the DataFrame to a Delta table, specifying the appropriate database and table names.

After loading the data into the Databricks Lakehouse, run validation checks to ensure data integrity and schema correctness. Use SQL commands or Spark DataFrame operations to compare the loaded data against expected values, ensuring that all records have been accurately imported and are ready for analysis.