Sync data from
Databricks to Iterable
Connect your data from Databricks to Iterable with Hightouch. No APIs, no months-long implementations, and no CSV files. Just your data synced forever.
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Integrate your data in 3 easy steps
Add your source and destination
Connect to 15+ data sources, like Databricks, and 140+ destinations, like Iterable.
Define your model
Use SQL or select an existing dbt or Looker model.
Sync your data
Define how fields from your model map to Iterable, 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 Iterable
Every email address you have in Iterable is a user or subscriber/contact. You can use Hightouch to update user information or delete, forget, or unforget users.
An event is an action a user takes on your website or app. They are specific to your business. Use Hightouch to send shopping cart, purchase, or custom events to Iterable.
When sending messages to your users, it's almost always necessary to include information about your organization's products, places, events, or services. Use Hightouch to send this information directly to Iterable.
Iterable's segmentation tool allows you to segment across all of your subscribers and to make static or dynamic lists. Use Hightouch to add users to lists to create highly targeted customer segments, so you can deliver the most relevant marketing messages.
Does this integration support in-warehouse planning?
Yes, if you integerate Databricks and Iterable 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 Iterable). 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 Iterable?
Lifecycle marketing platforms like Iterable are built on omnichannel experiences and multiple touch points. They allow marketers to experiment and iterate at a moment's notice. However, to build personalized experiences for your customers, you first need access to the rich behavioral data in your data warehouse.
This might include data models built around unique objects in your business like workspaces, subscriptions, playlists, or even custom audiences your data team has defined based on core metrics like items in cart, pages viewed, average order value, time spent in-app, etc.
To truly convert new users and retain existing customers, you need to build customized experiences that feel unique to each user. Usually, this includes sending custom emails, SMS messages, push notifications, or even web notifications.
For example, if you want to encourage users who abandoned their shopping cart in the last seven days to complete their purchase, you might want to send an email letting them know your stock is running low. Maybe you want to target new users with a special offer through an in-app/on-browser notification, or perhaps you simply want to send an SMS notifying your customers that their order has shipped.
Either way, the only complete 360-degree view of your customer lives in your warehouse. Your lifecycle marketing tool is only as good as the data you give it. If you want to offer the best possible experience to your customers, you need to take advantage of the data in your warehouse.
Why should you use reverse ETL to connect Databricks and Iterable data?
Whenever your marketing team wants to launch a new campaign, the typical process is to request a specific data set from your data team (e.g., can you give a list of every user who's visited our pricing page over the last seven days?) One-off requests like these are frequent, and each one pulls away from high-value work your data team could be doing.
To facilitate this, your data engineers must download ad-hoc CSV files so your marketing team can upload that data into Iterable. Alternatively, your data team might build and maintain custom pipelines to consistently ingest that data into Iterable. The problem is CSV files aren't fresh, and data pipelines are prone to failure because integrating with third-party APIs is hard.
Ultimately, your marketers want to self-serve, and that's only possible with Reverse ETL. With Hightouch, you can leverage your existing data models (e.g., lifetime value, last-login date, workspaces, annual recurring revenue, etc.) in your warehouse and sync that data directly to fields in Iterable. You can schedule your syncs to run sequentially or define a set cadence (e.g., running your syncs while your marketing campaign is in progress.)
Hightouch has a visual audience builder so your marketers can self-serve and build custom audiences using the parameters your data team has set. If any of your rows fail, Hightouch will automatically retry them later in the next sync. You can easily view all of your API payload requests/responses in a live debugger, and your sync logs can be written back directly to Snowflake. Ultimately your lifecycle marketing tool is only as good as the data you give it.
Sync data about users and accounts into Iterable to build hyper-personalized campaigns
Automatically update your Iterable segments with fresh data from your warehouse
Deliver better experiences by bringing in data from other customer touchpoints into Iterable
Monitor campaign revenue from purchases or cart-additions
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
Iterable is an integrated, cross-channel platform—built for marketers, trusted by engineers, designed with intelligence.Learn more about Iterable
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