Monday to Google BigQuery

Data Integration & ETL

Automatically Export & Sync Monday Data to Google BigQuery

Monday
Google BigQuery
Full data sets
No instructions needed
No code
No complicated flow charts
No triggers
Set up in under 3 minutes
Flat pricing starting at $19 /month

Data sets

ActivityLogs-AllTime by Board
Filterable
ActivityLogs-Last X days by Board
Date Rangable
Filterable
Items+Subitems by Board
Filterable
Pre-blended combo set SELF-LEFT-JOINED
Updates by Board
Filterable

Built for Synchronization

This service is designed to help anyone continuously export (i.e. "sync") bulk data from Monday.

If you only need to export your data one time, check the website of Monday to see if their service offers a one-time export feature natively. Sometimes a Data Source's native export feature may provide not as much data as its API does, in which case Flatly is the solution.

Additional Information

Flatly uses official API channels to provide an integration with Monday.
Monday.com is a Work Operating System (Work OS) that powers teams to run projects and workflows with confidence.
Simplified Layout

Monday.com's GraphQL API outputs data in such a way that a specialized service like Flatly, capable of formatting for a tabular layout, is recommended. Flatly's transformation of Monday.com's Items and Subitems for business intelligence applications is one of the best in the industry. It makes pagination and labeling completely automatic. This data set can be synced in a left-joined layout, with each Subitem occupying its own row alongside repeated parent Items, or in a standard layout with Subitems simply appended as many numbered columns for each parent Item which occupies a unique row.

Scopes

Flatly's access to Plaid and Plaid's downstream connected financial institutions is read-only access to text. It is limited to account data (words, numbers, dates). It does not include any money-movement, transfer capabilities or account holder profiles/identities.

Plaid alone interfaces directly with financial institutions, caches data from those institutions on Plaid infrastructure, and then makes the appropriate scoped subset of that data available to partners like Flatly using secure connections called client libraries.

Schema Simplicity

Flatly leverages intelligent schema generation that adapts to BigQuery's auto-detection protocols, handling data type inference and column ordering automatically so you never have to manually map fields.

Sanitized Data

Data hygiene is built directly into the ingestion process. Before a single row is written to BigQuery, Flatly validates your data against a generated reference schema. It proactively detects and strips out "schema violators" —inconsistent data types or malformed records that would typically cause a load job to crash. Flatly logs these exclusions in a detailed metadata file, ensuring your pipeline remains resilient and continuous while giving you full transparency into data quality issues.

Performance

Designed for scale and flexibility, the engine processes data in memory-optimized chunks to handle massive datasets efficiently. It offers powerful pre-load controls, allowing you to filter rows based on custom logic (e.g., date ranges, specific values) and allowlist specific keys. This ensures that you only sync high-value data, optimizing your BigQuery storage costs and query performance. Everything is secured via Service Account authentication, providing a stable, production-grade connection for your most critical analytics dashboards.

Looker reads tables from Google BigQuery.

File Formats

NDJSON

Integration Flow

Setup

Step 1

Select Monday from the Data Source dropdown and Google BigQuery from the Data Destination dropdown.

Step 2

Authorize Monday with your credentials using the OAuth flow.

Step 3

Authorize Google BigQuery with your credentials using the Service Account flow.

Step 4

Select your Data Set from the 4 Data Sets available using the dropdown.

Step 5

Click Flatten to sync your data.

Walkthrough Video

Extended Data Sources

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