How to load data from Netsuite to BigQuery

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

Try Airbyte Cloud Manual method (the hard way)

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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
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 Netsuite 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 Netsuite 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 Netsuite 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 Cloud Manual 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.

Try Airbyte Cloud Manual method (the hard way)

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.

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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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: Understand NetSuite API Access

Before you can extract data from NetSuite, familiarize yourself with the NetSuite SuiteTalk Web Services. This API allows you to programmatically retrieve data. Ensure you have the necessary permissions and access credentials for using SuiteTalk Web Services, which include an account ID, consumer key, consumer secret, token ID, and token secret.

Step 2: Set Up a Local Development Environment

Install the necessary software development tools on your local machine. This typically involves setting up a programming environment such as Node.js, Python, or Java, which are commonly used languages to interact with NetSuite APIs. Ensure you have access to libraries that can handle HTTP requests and OAuth 1.0 for authentication.

Step 3: Extract Data from NetSuite

Using your chosen programming language, write a script to authenticate using OAuth 1.0 and fetch data from NetSuite. Use the SuiteTalk Web Services to query the data you need. Construct your SOAP requests to pull data from specific records or custom saved searches. Test the script to ensure it correctly retrieves the data in a structured format like JSON or CSV.

Step 4: Transform Data for BigQuery Compatibility

Once you have extracted data from NetSuite, transform it into a format compatible with BigQuery. This may involve cleaning the data, ensuring correct data types, and preparing it into a structured format such as CSV or JSON that BigQuery can ingest. Consider using scripts or tools to automate this transformation process as needed.

Step 5: Set Up Google Cloud SDK

Install and configure the Google Cloud SDK on your local machine. This will allow you to interact with BigQuery using command-line tools. Authenticate the SDK with your Google Cloud account, ensuring you have the necessary permissions to create datasets and tables in BigQuery.

Step 6: Load Data into BigQuery

Use the `bq` command-line tool provided by the Google Cloud SDK to load your transformed data into BigQuery. Create a new dataset and table if they do not already exist. Use the `bq load` command to import your CSV or JSON file into the BigQuery table. Ensure the schema matches the data structure you prepared in the transformation step.

Step 7: Automate the Process

To maintain up-to-date data in BigQuery, automate the entire process. Set up a cron job or use a task scheduler to run your extraction, transformation, and loading scripts at regular intervals. This ensures that your BigQuery dataset remains current without manual intervention.
By following these steps, you can effectively move data from NetSuite to BigQuery without the need for third-party connectors or integrations.