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Facebook Pixel

Integrate your Databricks Data Warehouse with Facebook Pixel

Don't go through the pain of direct integration. RudderStack’s Reverse ETL connection makes it easy to send data from your Databricks Data Warehouse to Facebook Pixel and all of your other cloud tools.

Easy Databricks to Facebook Pixel integration with RudderStack

RudderStack’s open source Reverse ETL connection allows you to integrate RudderStack with your your Databricks Data Warehouse to track event data and automatically send it to Facebook Pixel. With the RudderStack Reverse ETL connection, you do not have to worry about having to learn, test, implement or deal with changes in a new API and multiple endpoints every time someone asks for a new integration.

Popular ways to use Facebook Pixel and RudderStack

Easily send conversions

Send existing events to Facebook Pixel as conversions with no additional code.

Track client and server-side

Manage client-side and server-side conversions for Facebook Pixel with a few clicks.

Easily send audience data

Send custom audiences from your warehouse to Facebook Pixel.

Frequently Asked Questions

With Rudderstack, integration between Databricks and Facebook Pixel is simple. Set up a Databricks source and start sending data.
Pricing Databricks and Facebook Pixel can vary based on the way they charge. Check out our pricing page for more info. Or give us a try for FREE.
Timing can vary based on your tech stack and the complexity of your data needs for Databricks and Facebook Pixel.

Use the Facebook Pixel integration with other popular sources

About Facebook Pixel

Facebook Pixel (now Meta Pixel) is a developer tool by Facebook that allows you to track any visitor activity on your website. It’s a JavaScript snippet that you can add to your website and start tracking visitor actions such as page views, clicks, and add-to-cart events. The tracked metrics, also called events, appear on your Meta Pixel page in Events Manager.

About Databricks

Storage layer that offers reliability and security on your data lake for streaming and batch operations