How to Sync BigCommerce to Google BigQuery

Whether you want to analyze sales trends, track inventory, or combine ecommerce data with marketing performance, BigQuery gives you the flexibility to work with your BigCommerce data at scale.

How to Sync BigCommerce Data to Google BigQuery

BigCommerce provides tools for managing your online store, but as your business grows, you may need more flexibility than built-in reports can offer. Google BigQuery is a cloud-based data warehouse designed to store and analyze large datasets. By syncing your BigCommerce data to BigQuery, you can run complex SQL queries, build custom dashboards, and analyze your store’s historical performance without manually exporting CSV files.

This lets operators, analysts, and marketers bring orders, products, customers, and inventory data together in one place. You can combine BigCommerce sales with Google Ads, Facebook Ads, or other marketing data to understand what is driving revenue, identify trends, and make better business decisions.

Use Cases and Benefits

Syncing BigCommerce data to BigQuery opens up possibilities beyond basic store reporting. Some key use cases include:

• Advanced Analytics & Reporting: BigQuery lets you run complex queries across your ecommerce data and combine it with other business sources. For example, you could join order data with Google Ads costs to measure marketing ROI, analyze revenue by product category, or build a Looker Studio dashboard that updates automatically. You can also use SQL to analyze repeat purchases, customer lifetime value, and sales trends over time.
• Scalability: Spreadsheets can become difficult to manage as order volumes and historical data grow. BigQuery is built to handle large datasets, allowing you to query years of orders, line items, and customer data without relying on a collection of increasingly large spreadsheets.
• Inventory & Operational Insights: BigCommerce data can help you go beyond revenue reporting. By analyzing product variants, inventory levels, shipments, and order transactions, you can investigate stock availability, fulfillment performance, and changes in product demand.
• Data Consolidation & Backup: If you sell across multiple storefronts or channels, BigQuery can serve as a central place to bring your data together. You can combine BigCommerce data with information from other ecommerce platforms, fulfillment providers, and advertising tools. Keeping a copy of your store data outside BigCommerce can also be useful for historical analysis and internal reporting.

Manual Export of BigCommerce Data

BigCommerce allows you to export store data to CSV files through its control panel. For example, you can export orders from Orders → Export Orders and product catalog data from Products → Export.

Once you have downloaded the files, you can create tables in BigQuery and upload the data using the Google Cloud console or a BigQuery load job. You can repeat this process for different types of store data as needed.

While manual exports are useful for occasional reporting, they have some drawbacks. Every time you need fresh data, you must generate another export and load it into BigQuery. Keeping historical records consistent can also require extra work, particularly when you are dealing with multiple datasets or large order volumes.

If you want your dashboards and reports to stay up to date, repeating these steps can quickly become time-consuming.

Automating BigCommerce to BigQuery with SyncRange

Instead of manually exporting CSV files, SyncRange can automate syncing your BigCommerce data into BigQuery on a schedule. The setup is straightforward and requires no coding.

• Connect BigCommerce to SyncRange: Install SyncRange from the BigCommerce App Marketplace. Approve the read-only permissions requested during installation, then create a SyncRange account or sign in to your existing account. SyncRange will connect to your store so you can select the data you want to export.
• Connect BigQuery: In SyncRange, go to Connections → Google and add a BigQuery destination. Select your Google Cloud project and BigQuery dataset. This connects SyncRange to the destination where your store data will be loaded.
• Configure the Export: Open Export Builder and create a new export. Choose BigCommerce as the data source, select your connected store, and choose BigQuery as the destination. Give the export a name, then select the data you want to sync.
• Choose Your Data and Fields: SyncRange supports a range of BigCommerce data, including Orders, Order Line Items, Order Shipping Addresses, Order Shipments, Order Transactions, Customers, Products, Product Variants, Categories, Brands, Coupons, Gift Certificates, Inventory Locations, Inventory Levels, Channels, and Product Channel Assignments. Select the data tabs and fields relevant to your reporting needs.
• Set the Date Range and Update Behaviour: For order-related data, choose the date range you want to export. You can use a preset such as “Since last export” for incremental order exports. Catalog, inventory, and channel data exports return the full current dataset on each run. For each data tab, choose whether to append rows or replace the existing destination data.
• Save, Test, and Schedule: Save the export and run it manually to validate the results in BigQuery. Once everything looks correct, set a schedule from the export detail page. SyncRange will use your configured data selections and date ranges for future scheduled runs.

Once the export is configured, SyncRange handles retrieving the selected BigCommerce data and sending it to BigQuery. You can spend less time downloading files and maintaining data pipelines, and more time analyzing your business.

Automating the Sync Provides Clear Benefits

• Saves Time & Effort: No more repeatedly downloading CSVs, uploading files, or copying data between systems. Once the export is configured, SyncRange handles the recurring work so you can focus on sales, operations, and analysis.
• Reduces Manual Errors: Automated transfers reduce the risk of mistakes caused by repeated manual exports, missing files, or inconsistent data-handling steps. Your export also follows the field selections and settings you configured.
• Keeps Reports Up to Date: Set a schedule to refresh your BigQuery data automatically. Your dashboards and queries can use the latest exported information without requiring someone to run the process manually each time.
• Supports Better Reporting: Store your BigCommerce data in one warehouse and combine it with other sources, such as advertising and fulfillment data. This gives finance, operations, and marketing teams a shared dataset for more consistent reporting.
• Gives You More Control Over Your Data: Choose the data tabs, fields, date ranges, and append-or-replace behaviour that suit your reporting requirements. You can start with orders and products, then add other datasets as your analysis becomes more sophisticated.

SyncRange is designed to make ecommerce data exports accessible without requiring you to build and maintain your own API integration. Connect your BigCommerce store to BigQuery, configure your exports, and automate the flow of data on a schedule.

With a free plan available, you can get started without a complex setup or manual CSV workflow.

Start free with SyncRange and start syncing your BigCommerce data to BigQuery.