How to load data from Insightly to BigQuery

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

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams  to meet all their growing custom business demands in the new AI era.

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
  • 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 Insightly 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 Insightly 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 Insightly 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.

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.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

Simple & Easy to use Interface

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.

Guided Tour: Assisting you in building connections

Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.

Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes

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.

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.

An Extensible Open-Source Standard

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.

Full Control & Security

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.

Enterprise Support with SLAs

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 Insightly

Begin by exporting your data from Insightly. Log in to your Insightly account, navigate to the data section you want to export (such as contacts, leads, etc.), and use the export feature to download the data in CSV format. Ensure you have the necessary permissions to perform data export.

Step 2: Prepare CSV Files for Import

Once you've exported the data, review the CSV files to ensure they are correctly formatted for import into BigQuery. Check for any inconsistencies or errors in the data, such as incorrect data types or missing values, and clean the data as needed.

Step 3: Set Up a Google Cloud Project

If you haven't already, create a Google Cloud Project. Go to the Google Cloud Console, sign in with your Google account, and create a new project. This project will be used to manage your BigQuery resources.

Step 4: Create a BigQuery Dataset

Within your Google Cloud Project, navigate to BigQuery. Create a new dataset where your Insightly data will be stored. Ensure you specify the location and other settings according to your requirements.

Step 5: Define the BigQuery Table Schema

Define the schema for your BigQuery table based on the structure of your CSV files. In BigQuery, each column needs a name and data type. You can define the schema manually or use a schema auto-detection feature when loading data.

Step 6: Upload CSV Files to Google Cloud Storage

Before loading data into BigQuery, upload your CSV files to Google Cloud Storage (GCS). Access the GCS section in the Google Cloud Console, create a bucket, and upload your CSV files. Make sure the files are accessible to your BigQuery project.

Step 7: Load Data from GCS to BigQuery

Finally, load the data from Google Cloud Storage into BigQuery. Use the BigQuery Console or the bq command-line tool to execute a load job. Specify the source data (GCS file paths), the destination table (in your dataset), and the schema. Once the load job completes, verify the data in BigQuery to ensure it matches the exported data from Insightly.

By following these steps, you can effectively transfer data from Insightly to BigQuery without relying on third-party connectors.