Pull from BigQuery

How to authenticate

BigQuery uses Google authentication to authorize the connection.

• In your Parabola flow, add a Pull from BigQuery step.
• Open one of the Pull from BigQuery steps and click Authorize and sign in to your Google account, giving Parabola the requested permissions.
• Each Pull from BigQuery step can re-use your authentication; make sure each step is configured to use your credentials.

Parabola will securely store your credentials and use them to authenticate each request to BigQuery.

Available data

Using the BigQuery integration in Parabola, you can run custom SQL queries against any dataset and table in your BigQuery projects. Write Standard SQL queries to filter, aggregate, join, and transform data from your BigQuery tables—try using the chat interface on the left-hand side of your canvas to help write your SQL queries.

Here’s how to use the available connections to run a SQL query:

• Use the List Projects step if you need to find a BigQuery project ID. Increase the "Total pages limit" value if you need to see more projects.
• Use the Send Query (Run a SQL query) and Pull results from BigQuery (Poll for query results) steps to submit a query and get all results. The Custom transform will create a table of results from your query results.

The integration supports full SQL query capabilities, allowing you to leverage BigQuery's powerful analytics engine to prepare data exactly as needed before pulling it into Parabola.

Common use cases

• Automate recurring reports daily or weekly, then export results to Google Sheets, email them to stakeholders, or push them to other systems.
• Combine BigQuery analytics data with operational data from systems like Shopify, NetSuite, or your warehouse management system to create unified views and spot discrepancies.
• Query BigQuery tables on a schedule to check for data freshness, validate row counts, and flag anomalies—then send alerts via Slack or email when issues are detected.
• Pull summarized data from BigQuery and combine it with real-time metrics from other sources to create comprehensive dashboards that update automatically.
• Run complex analytical queries in BigQuery, pull the results into Parabola, and use them to trigger downstream workflows like updating inventory systems or sending targeted communications.

Tips for using Parabola with BigQuery

• Schedule your flows strategically to run on the cadence your team needs: hourly for real-time dashboards, daily for standard reporting, or weekly for executive summaries.
• Write efficient SQL queries to filter, aggregate, and transform data in your SQL query before pulling it into Parabola. This reduces data transfer, speeds up your flow, and optimizes BigQuery costs.
• Leverage parameterized queries: When building dynamic queries based on dates or other variables, use BigQuery's parameterized query features for better performance and security.
• Handle pagination for large results: For queries returning many rows, use the pagination settings in Parabola to efficiently retrieve all results without timeout issues.
• Validate your BigQuery data with steps in Parabola that flag unexpected values, missing data, or anomalies before the data flows to downstream systems.
• Document your SQL logic with cards in Parabola to explain what each query does and why specific filters or transformations are applied, making it easier for teammates to maintain your flows.

By connecting BigQuery with Parabola, you transform your data warehouse into an active part of your operational workflows—automating reports, reconciliations, and data-driven decisions without manual exports or custom code.