How to load data from Monday to BigQuery

Learn how to use Airbyte to synchronize your Monday data into BigQuery within minutes.

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Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Monday connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Monday data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Monday to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Try Airbyte CloudManual method (the hard way)

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

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Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

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What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

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Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

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Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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Chase Zieman

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“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

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Operational Intelligence Manager

"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Export Data from Monday.com

Begin by exporting the data you need from Monday.com. Navigate to your board on Monday.com, click on the three dots menu at the top right of the board, and choose the 'Export' option. Select 'Export to Excel' or 'Export to CSV' depending on your preference. Save the exported file to your local system.

Review the exported file to ensure that the data structure aligns with what you need in BigQuery. Open the file in a spreadsheet program like Excel or Google Sheets and clean up any unnecessary columns or rows. Ensure the data types (e.g., strings, integers) are consistent and match the expected schema in BigQuery.

Log in to your Google Cloud Platform account and navigate to BigQuery. Create a new dataset by clicking 'Create Dataset' in the BigQuery console. Give your dataset a unique name and configure any necessary settings like data location and expiration.

Prepare the schema for the table where the data will be imported. This involves specifying the names, data types, and modes (e.g., NULLABLE, REQUIRED) for each column. You can do this manually in the BigQuery console under the dataset you created by selecting 'Create Table' and defining the schema in the UI.

Before loading data into BigQuery, you need to upload it to Google Cloud Storage (GCS). Access Google Cloud Storage from the GCP console and create a new bucket or use an existing one. Upload your CSV or Excel file to this bucket by clicking 'Upload files' and selecting your file.

With your data in GCS, return to BigQuery and load the data from your GCS bucket. In the BigQuery console, navigate to your dataset, click 'Create Table', and select 'Google Cloud Storage' as the source. Enter the GCS URI of your uploaded file. Configure any additional settings, such as field delimiter or skipping header rows, and confirm the schema matches your data.

Once the data import is complete, run a few queries in BigQuery to verify that the data matches what you expected. Check for discrepancies in row counts, data accuracy, and data types. This step ensures that the data transfer from Monday.com to BigQuery was successful and that the dataset is ready for analysis.

By following these steps, you can efficiently transfer data from Monday.com to BigQuery without the need for third-party connectors or integrations.