Sync BigQuery to Vitally
Prevent churn and give your team a 360° view of their accounts
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Integration in Four Steps
Step 1: Connect BigQuery
Connect BigQuery with standard API, ODBC and JDBC credentials.
Step 2: Connect Vitally as a destination
You can connect to Vitally by creating an API key in Vitally and providing it to Census in an intuitive interface.
Step 3: Define the core data that matters for your business
Write a SQL Statement. Select the records you want to sync from BigQuery. Census will match records based on the unique identifier you provide (like email or ID).
Step 4: Schedule your sync
Choose your sync frequency with options to transfer data in real time, on a schedule, or triggered via our API.
Get a demoWhere can you sync your BigQuery in Vitally?
A single source of truth in all your tools
Census is the easiest way to share a consistent view of your data across your entire business. Define your models, metrics, and business logic in your data warehouse and sync them to all your operational tools.
- Create trust in consistent metrics
- Automate confidently with clean data
- Simplify your data integration strategy
- Improve data security and visibility
Helping business & data teams collaborate at
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Advantages of Census
Build once. Reuse everywhere.
Once you define your models in dbt or in Census, you can sync them to all your tools, so every team is using the same data to achieve their goals.
No more CSV files & Python scripts
Focus on improving data quality and collaboration, not writing custom code to integrate with Go-t0-Market tools. Census takes care of getting the data into the hands of your business team. No engineering favors required.
It just works. At scale.
Whether you sync 100 records or 100 billion, we will keep your data in sync across your warehouse and your business tools. Census automatically navigates API failures and monitors errors to keep you aware of worst case scenarios.
Census is the #1 Data Activation and Reverse ETL platform
“Census is a big part of how we drive net new business and greatly reduce costs using data.”
