Sync data from
Databricks to Snapchat
Connect your data from Databricks to Snapchat with Hightouch. No APIs, no months-long implementations, and no CSV files. Just your data synced forever.
Trusted by data teams at
Trusted by data teams at
Integrate your data in 3 easy steps
Add your source and destination
Connect to 15+ data sources, like Databricks, and 150+ destinations, like Snapchat.
Define your model
Use SQL or select an existing dbt or Looker model.
Sync your data
Define how fields from your model map to Snapchat, and start syncing.
Model your Databricks data using any of these methods
dbt Model Selector
Sync directly with your dbt models saved in a git.
Create and Edit SQL from your browser. Hightouch supports SQL native to Databricks.
Select available tables and sheets from Databricks and sync using existing views without having to write SQL.
For less technical users, pass traits and audiences from Databricks using our visual segmentation builder.
Where can you sync your Databricks data in Snapchat
Create and update Customer List segments within a specified ad account.
Sync conversion events to help Snap optimize your ad campaigns, improve your targeting and measure the conversions that resulted from your Snapchat campaigns.
Does this integration support in-warehouse planning?
Yes, if you integerate Databricks and Snapchat using Hightouch, in-warehouse planning is supported.
Great, but what is in-warehouse planning?
Between every sync, Hightouch notices any and all changes in your data model. This allows you to only send updated results to your destination (in this case Snapchat). With the baseline setup, Hightouch picks out only the rows that need to be synced by querying every row in your data model before diffing using Hightouch’s infrastructure.
The issue here is this can be slow for large models.
Warehouse Planning allows Hightouch to do this diff directly in your warehouse. Read more on how this works here.
Why is it valuable to sync Databricks data to Snapchat?
Thanks to Databricks, it's easier than ever to access your customer data, run complex queries, and segment your customers/users into various categories or audiences.
However, to target users and send conversion data to Snapchat, you need access to all of the unique behavioral data (e.g., last-login date, items in cart, pages viewed, etc.) and core business metrics like lifetime value, workspaces, subscriptions, annual recurring revenue, etc. that lives in Databricks. You then need to be able to package this data to send to Snapchat in the format expected. Plus, you want to do this in a real-time and automated way.
Snapchat is only as good as the data you give them, and if you truly want to optimize your ad spend, increase your match rates, and drive conversions, you need to provide custom data from Databricks within your warehouse.
Maybe you want to retarget users who abandoned their shopping cart in the last seven days, or upload a list of high-value customers to identify potential lookalike audiences, or perhaps you want to upload offline conversion events to reduce your customer acquisition costs and increase your return on ad spend.
Why should you use reverse ETL to connect Databricks and Snapchat data?
In the past, uploading customer data to Snapchat meant hopping back and forth between your various SaaS applications or asking your data team for CSV files. Neither of these options is preferable because marketing teams want to self-serve, and data teams don't enjoy constantly fulfilling one-off marketing requests.
Even worse, if you truly want to optimize your advertising campaigns, you need to be uploading fresh data consistently. Non-fresh data can be expensive for ads. If you're using CSVs to define who to exclude from paid ads and you're uploading that data weekly, that's potentially one week of irrelevant ads. As a workaround, engineering teams will integrate directly with the Snapchat API, and build and maintain custom in-house pipelines. The problem is that a single API change can break everything, and data engineers don't want to spend their time building and maintaining pipelines.
With Hightouch, you can leverage the existing data models and customer segments your engineering team has defined in your warehouse and sync that data directly to your ad platforms in real time. You can schedule your data syncs to run automatically, on a set cadence, or even for the exact duration of your marketing campaign. Hightouch lets your data teams establish the guardrails for your marketers to self-serve and build custom audiences through a drag-and-drop interface.
Upload lists to Snap Audience Match to run ads based on certain attributes within your database, such as people who have visited your site
Create audiences on Snap using subsets of your users rather than all of them
Continuously fuel your Snapchat custom audiences with live data so that data remains fresh
Databricks is a data science and analytics platform built on top of Apache Spark. Databricks implement the Data Lakehouse concept in a single unified, cloud based platform.Learn more about Databricks
Snap Audience Match (SAM) is a targeted advertising service that allows businesses to import information or “identifiers” about leads and customers (such as email and phone number) to retarget them via ads on Snapchat.Learn more about Snapchat
Other Databricks Integrations
Other Snapchat integrations
Hightouch Playbooks: Best practices to leverage reverse ETL
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increase in return on ad spend
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