How to load data from Commercetools to BigQuery

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

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Building your pipeline or Using Airbyte

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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 Commercetools 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 Commercetools 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 Commercetools 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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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.

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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.

Try Airbyte CloudManual 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.

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: Set Up API Access in commercetools

Begin by setting up API access in commercetools. Log in to your commercetools account and navigate to the Developer Center. Create a new API client with the necessary permissions to access the data you need. Note down the client ID, client secret, project key, and API URL, as you'll need these credentials to authenticate your requests.

Use the commercetools API to authenticate and fetch the required data. You can do this using a programming language like Python, JavaScript, or any other language that supports HTTP requests. Make sure to handle pagination to retrieve all records if your dataset is large. Store the fetched data temporarily in a local storage or a staging area.

Transform the data into a format compatible with BigQuery, such as CSV, JSON, or Avro. Ensure that the data types and structures match the schema of the BigQuery table where you'll load the data. This step may require writing scripts to map and convert data fields appropriately.

Set up your Google Cloud environment if you haven't already. Create a Google Cloud Project and enable the BigQuery API. Ensure that you have the necessary permissions to create datasets and tables in BigQuery. Set up a service account with appropriate roles, such as BigQuery Data Editor.

Upload the transformed data files to Google Cloud Storage. This serves as an intermediary step, as BigQuery can load data directly from GCS. Use the Google Cloud SDK or a REST API to upload your files to a GCS bucket. Ensure that your bucket is in the same location as your BigQuery dataset for optimal performance.

Use the BigQuery Data Transfer Service to load the data from GCS into BigQuery. You can accomplish this using the BigQuery Console, the bq command-line tool, or a BigQuery API call. Specify the source file(s), the destination dataset, and table. Make sure to configure the load job to match the data schema, including any options for data handling, such as field delimiters for CSV files.

Once the data is loaded into BigQuery, verify that it has been imported correctly. Check for any discrepancies or errors in the data. Perform queries to ensure data integrity and accuracy. After verification, clean up temporary files from local storage and GCS to optimize storage usage and maintain security.

Following these steps will help you move data from commercetools to BigQuery without relying on third-party connectors or integrations.