Sync data from
Databricks to Correlated
Connect your data from Databricks to Correlated 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 Correlated.
Define your model
Use SQL or select an existing dbt or Looker model.
Sync your data
Define how fields from your model map to Correlated, 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.
Does this integration support in-warehouse planning?
Yes, if you integerate Databricks and Correlated 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 Correlated). 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 Correlated?
Correlated is the backbone of your sales org. It's where you manage all of your contacts, accounts, and deals. The problem is that most of your CRM data has to be manually input by individual sales reps, so it doesn't show you an accurate 360-degree view of your customer. You only have access to basic information and historical interactions.
All of your Correlated data, in addition to the rich behavioral data captured through your app/website already, exists in Databricks. Most of the time, this includes core metrics your data team has defined around lifetime value, workspaces, subscriptions, playlists, average order value, last login date, etc. There's a high chance you're probably even consuming this data in a dashboard, and your sales reps can't answer key questions like:
- Which accounts have the highest lifetime value?
- Which users have the highest utilization in our product?
- When did user X last log in to the app?
- Which leads/accounts should I be prioritizing?
- Which accounts are at risk of churning?
- What is the average order value of account X?
Your sales team doesn't want to hop back and forth between your various SaaS tools to answer these questions. They want to take action in Correlated, and that means enriching your CRM with data directly from your warehouse.
Why should you use reverse ETL to connect Databricks and Correlated data?
Conventionally moving data from Snowflake to Correlated meant downloading ad hoc CSV files and uploading them manually or forcing your data team to integrate with the Correlated API and build and maintain custom pipelines. In reality, CSVs are not scalable, and in-house data pipelines and custom scripts break constantly.
Other point-to-point solutions create a weave complex web of pipelines to and from various SaaS applications and customer data platforms (CDPs), forcing you to pay for another layer of storage in addition to your data warehouses.
Reverse ETL solutions like Hightouch query against Snowflake and sync that data directly to Correlated. You don't have to worry about CSVs or APIs. You can leverage your existing tables, data models, and audience segments. All you have to do is define your data and map it to the appropriate columns in Correlated. You can schedule your syncs to run manually or even trigger them to run sequentially based on criteria that you define.
Hightouch automatically diffs data between syncs to ensure your only ever syncing the freshest data, and if any rows fail, Hightouch will automatically retry them later. A live debugger lets you analyze your API payload requests/responses and failed runs in real-time.
Add, remove and update users and accounts to route leads to the right internal team
Sync events to discover trends about what drives conversion, expansion and churn
Sync up to 100 custom fields your team uses for specialized workflows
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
Learn more about Correlated
Other Databricks Integrations
Other Correlated integrations
Hightouch Playbooks: Best practices to leverage reverse ETL
Read more about Hightouch
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Operational Analytics shifts the focus from simply understanding data to taking action on it in the tools that run business processes. Instead of using dashboards to make decisions, Operational Analytics is focused on turning insights into action – automatically.Read
Activate data to any of your marketing and advertising tools
This might be one of the greatest inventions for technical marketers since the advent of legacy CDPs back in 2015.
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increase in return on ad spend
improvement in email engagement
lift in customer acquisition