Sync multi-bank balance data to BigQuery using Plaid
Streamline your financial oversight by automatically consolidating real-time balances from various institutions like Amex and PayPal into a centralized BigQuery warehouse. This automated pipeline bridges the gap between diverse banking APIs and structured data analytics, ensuring your reporting is always up-to-date. It is the ultimate tool for finance professionals who need a unified view of their organization's liquidity without manual data entry.
Run this with your team's AIWhat This Recipe Does
This automated financial data pipeline streamlines the process of moving critical fiscal information from external platforms directly into Google BigQuery. By eliminating manual data exports and spreadsheet reconciliations, your team can maintain a real-time view of financial health without the risk of human error. The automation runs on a scheduled basis, ensuring that your data warehouse is always populated with the most recent transaction records, currency fluctuations, or expense reports. This process handles complex data transformations automatically, normalizing various data formats into a clean, structured schema ready for immediate analysis. Business leaders can rely on this automation to fuel their BI dashboards, enabling faster decision-making based on accurate, up-to-the-minute financial insights. Instead of spending hours every week downloading CSV files and cleaning data, your finance department can focus on high-level strategy and forecasting. This solution bridges the gap between raw financial data sources and your centralized data warehouse, providing a robust foundation for scalable financial operations and transparent reporting.
What your team gets
Forms and dashboards, so it is not a script only one person understands
Runs on your schedule in the cloud, so it does not stop when a laptop closes
Endpoints, so the rest of your stack can trigger the same work
DaySchedule connected for the team, not per person
How It Works
- 1
Open the recipe and connect your accounts
Connect DaySchedule once, in your team cloud, and nobody has to do it again on their own machine
- 2
Tell your own agent what is different about your process
Claude, ChatGPT, Cursor, whichever your team already uses. It adapts the recipe to how you actually work
- 3
Run it, then leave it running
It lives in your team cloud, so it keeps going after you close the laptop and every teammate's AI can use it
Who Uses This
- Financial Controllers use this to consolidate daily transaction data from payment gateways into BigQuery for month-end closing.
- Operations Managers use this to track real-time vendor spending by syncing procurement API data with centralized financial records.
- Data Analysts use this to automate the ingestion of currency exchange rates or market data for complex financial modeling and forecasting.
Frequently Asked Questions
Do I need to write SQL or code to use this?
No. While the workflow uses code for data normalization internally, the Runwork interface allows you to manage the automation and view results without technical expertise.
Can I change how often the data syncs?
Yes. The schedule trigger can be adjusted to run hourly, daily, weekly, or at any specific interval that suits your reporting needs.
Is my financial data secure during the transfer?
Yes. The automation uses secure HTTP requests and direct integrations with Google BigQuery, ensuring data is encrypted and handled according to enterprise security standards.
Can I connect this to other financial tools?
Yes. The HTTP request component is highly flexible and can be configured to pull data from any financial software or bank API that supports standard web connections.
Coming from n8n?
This recipe uses nodes like ScheduleTrigger, HttpRequest, SplitOut, Code and 3 more. On Runwork, you don't need to learn n8n's workflow syntax. Describe what you want to your own AI agent in plain English.
Based on n8n community workflow. View original
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Run this with the AI your team already uses
Your agent adapts it, your team cloud keeps it running, and everyone's AI can find it.
Open this recipe in Runwork