How to load data from Microsoft Dataverse to BigQuery

Learn how to use Airbyte to synchronize your Microsoft Dataverse 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.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
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  • 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 Microsoft Dataverse 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 Microsoft Dataverse 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 Microsoft Dataverse 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’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.

What our users say

Raman Singh

Tech Lead at Symend

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

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Chase Zieman

Chief Data Officer

“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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Rupak Patel

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 Microsoft Dataverse

Begin by exporting the data you need from Microsoft Dataverse. Use Power Apps to navigate to your Dataverse environment. From there, you can utilize the built-in export functionality to download tables or datasets as CSV or Excel files. This provides a manageable format for transferring data.

Once you have your data in CSV or Excel format, review and clean the data to ensure it matches the schema required by BigQuery. Check for inconsistencies, missing values, and ensure data types are consistent across columns. Save the cleaned file in a CSV format, as it's the most compatible with BigQuery.

Log in to your Google Cloud Platform (GCP) account and navigate to Google Cloud Storage. Create a new bucket where you will temporarily store your CSV file. Choose a unique and relevant name for your bucket that aligns with GCP naming conventions.

Once your bucket is ready, upload your prepared CSV file to Google Cloud Storage. Navigate to your bucket, click on “Upload Files,” and select your CSV file. This step is crucial as BigQuery can easily access data stored in Google Cloud Storage.

Go to the BigQuery console in GCP and create a new dataset. Datasets in BigQuery are logical containers for tables, and you need to ensure your naming is consistent with your project's naming conventions. This step is essential for organizing your data once imported.

In the BigQuery console, create a new table and select the option to load data from Google Cloud Storage. Specify the path to your CSV file in the bucket. During this step, configure the schema to match your data, setting the appropriate data types for each column. Ensure that you check the options for CSV, such as comma as the delimiter, and handle any necessary file encoding settings.

After loading the data, run a few queries in BigQuery to ensure the integrity and accuracy of the imported data. Check for any discrepancies or errors that might have occurred during the transfer process. This final verification step ensures that your data is ready to be used for analytics or other operations in BigQuery.