How to load data from Shopify to BigQuery
Learn how to use Airbyte to synchronize your Shopify data into BigQuery within minutes.
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Start syncing with Airbyte in 3 easy steps within 10 minutes
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.
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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 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.
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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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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
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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
- Create a Google Cloud Project:
- Go to the Google Cloud Console (https://console.cloud.google.com/).
- Click on the project drop-down and select “New Project”.
- Enter a project name and billing information as required.
- Enable BigQuery API:
- In the Cloud Console, navigate to “APIs & Services > Dashboard”.
- Click on “+ ENABLE APIS AND SERVICES”.
- Search for “BigQuery API” and enable it.
- Set up a BigQuery Dataset:
- Go to the BigQuery console.
- Click on your project name, then “Create Dataset”.
- Provide a dataset ID and set other options as necessary.
- Get API Access:
- Log in to your Shopify admin panel.
- Go to “Apps > Manage private apps”.
- Create a new private app and ensure it has the necessary permissions to access the data you want to export.
- Note the API key and password; you’ll need these to authenticate API requests.
- Extract Data from Shopify:
- Use Shopify’s REST Admin API to extract the data you want to move to BigQuery.
- Write a script (e.g., in Python) to make paginated API calls to retrieve data from endpoints corresponding to the data you need (e.g., orders, products, customers).
- Ensure that you handle rate limits and pagination correctly.
- Format the Data:
- The data retrieved from Shopify will be in JSON format. BigQuery requires data in a format it can ingest, like CSV, Avro, or JSON with a newline delimiter.
- Convert the data into a BigQuery-friendly format, ensuring that it matches the schema you plan to use in BigQuery.
- Create a Schema:
- Define a schema for your BigQuery tables that corresponds to the Shopify data you’re importing.
- Make sure data types in your schema match the data you extracted (e.g., STRING, INTEGER, FLOAT, TIMESTAMP).
- Create a Cloud Storage Bucket:
- In the Google Cloud Console, navigate to “Storage > Browser”.
- Click on “Create bucket” and follow the prompts to create a new bucket.
- Upload the Data Files:
- Use the gsutil command-line tool or the Cloud Console to upload your formatted data files to the newly created bucket.
- Create Tables in BigQuery:
- In the BigQuery console, select your dataset and click on “Create Table”.
- Set the “Create table from” option to “Google Cloud Storage” and provide the path to your uploaded files.
- Define your table schema, either manually or by selecting “Auto-detect”.
- Load the Data:
- Configure the remaining options for your data load job, such as file format and any necessary data conversion options.
- Click “Create Table” to start the load job.
- Monitor the job for completion and check for any errors.
Check the Loaded Data:
- Run queries against your new tables in BigQuery to ensure the data has been loaded correctly.
- Compare record counts and sample data with your original dataset to verify integrity.
- Automate Data Extraction:
- Use a scheduler like cron to run your data extraction script at regular intervals.
- Automate Data Upload and Load:
- Write a script that uploads new data files to Google Cloud Storage and triggers a BigQuery load job.
- Schedule this script to run after each data extraction process.
Remember to secure your data throughout this process by following best practices for handling API keys and ensuring that your Google Cloud resources are not publicly accessible.