From Shopify to BigQuery: managed, scheduled, no code.
Kondado replicates data from Shopify to BigQuery through 17 available pipelines covering 505 fields, including Orders, Customers, Products, Inventory, and Abandoned Checkouts. Users configure replication schedules ranging from 5 minutes to daily intervals, with data landing in BigQuery as structured datasets ready for SQL analysis and business intelligence.
Kondado provides a direct integration that lets you send Shopify data to BigQuery without writing code. Simply authenticate your Shopify store, configure your BigQuery destination, and select which data pipelines you need. The platform handles the replication automatically on your chosen schedule, whether you need updates every five minutes or daily batches. This approach eliminates manual exports and keeps your analytics warehouse current with fresh ecommerce data.
Once your data arrives in BigQuery, you can build comprehensive reports that combine transaction history with customer behavior analytics. Marketing teams gain visibility into campaign performance through UTM tracking, while operations managers monitor inventory levels and fulfillment status across multiple storefronts.
With Kondado’s Shopify to BigQuery pipeline, you can analyze the complete customer journey by combining the Abandoned Checkouts pipeline with Orders data to identify conversion bottlenecks and recover lost revenue. The Orders Details and Orders Line Items pipelines provide granular visibility into product performance, discount effectiveness, and seasonal trends across your catalog. Finance teams can leverage the Orders Transactions pipeline to reconcile payments and track cash flow directly within BigQuery, while marketing analysts use the Orders Visits UTM Parameters pipeline to attribute revenue to specific campaigns and optimize advertising spend.
Try out all the features for free for 14 days
Replicated to BigQuery
Shopify data available for BigQuery
Tables Kondado writes into your BigQuery, on a schedule you control.
17
available pipelines
505
extractable fields
BigQuery
Destination
Available integrations
Abandoned Checkouts
View data on abandoned checkouts, including total value, items in the cart, and abandonment date.
Customers
Track customer information, including name, email, status, and account creation date.
Events
Log important events, including event type, date, and details associated with each event.
Inventory: items
Monitor item inventory, including SKU, available quantity, and product location.
Order timeline events
Analyze order timeline events, including status, date, and actions taken during processing.
Orders
Examine order details, including order number, total value, status, and creation date.
Orders Details
Get detailed information about orders, including items, prices, quantities, and applied discounts.
Orders Discount Applications
View how discounts were applied to orders, including discount type and value.
Orders Discount Codes
Access information on discount codes used in orders, including code and discount value.
Orders Fulfillments
Monitor the status of order fulfillments, including fulfillment date and shipping method.
Orders Line Items
Includes information on products, quantities, and prices, along with fields such as SKU, product title, and applied discount.
Orders Note Attributes
Contains custom attributes associated with orders, such as customer notes, tags, and additional fields that assist in identification.
Orders Refunds
Records information about refunds, including amounts, reasons, and status, along with fields such as refund date and method used.
Orders Shipping Lines
Provides details on shipping options, including carrier, shipping cost, and estimated delivery time.
Orders Transactions
Includes data on financial transactions, such as amounts, payment methods, and payment status, along with timestamps.
Orders Visits UTM Parameters
Captures information on order visits, including UTM parameters such as source, medium, and campaign for marketing analysis.
Products
Contains details about products, including title, description, price, SKU, and images, along with information on variants.
Sync data automatically — no code, no manual exports.
1
Connect Your Shopify Store
Authenticate your Shopify store through Kondado's interface by providing your store credentials and permissions. The platform establishes a direct integration to your ecommerce data without requiring technical configuration.
2
Configure BigQuery Destination
Set up your BigQuery project and dataset as the destination, specifying where your Shopify data should land. Kondado supports standard Google Cloud authentication to ensure seamless data delivery to your warehouse.
3
Select Pipelines and Schedule
Choose from 17 available pipelines such as Orders, Customers, and Products, then define your update frequency from 5-minute intervals to daily batches. Your data begins replicating immediately according to the schedule you specify.
Try out all the features for free for 14 days
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Why Kondado?
Focus on extracting value from your data and leave the rest to us
Answers about sending Shopify data to BigQuery automatically
How does the Shopify to BigQuery pipeline work?▼
Kondado uses a direct integration to extract data from your Shopify store and load it into BigQuery on a schedule you configure. You authenticate your store once, select from 17 available pipelines, and the platform handles the replication automatically. Data lands in structured datasets that you can query immediately with standard SQL.
What Shopify data can I replicate to BigQuery?▼
Kondado offers 17 pipelines covering 505 fields, including Orders, Customers, Products, Inventory items, Abandoned Checkouts, and Events. You can also access detailed financial data through pipelines like Orders Transactions, Orders Refunds, and Orders Discount Applications to support comprehensive ecommerce analytics.
How often does Shopify data update in BigQuery?▼
You configure the update frequency based on your business needs, choosing intervals from every 5 minutes to daily schedules. This flexibility lets you balance data freshness with processing costs, ensuring your BigQuery warehouse contains current information without unnecessary API calls.
What format does Shopify data arrive in BigQuery?▼
Data arrives as structured datasets organized by pipeline, with preserved data types and relationships intact. Each pipeline becomes a separate dataset in your BigQuery project, maintaining fields like timestamps, IDs, and monetary values in formats ready for SQL analysis and connection to Looker Studio or Power BI.
Can I combine Shopify data with other sources in BigQuery?▼
Yes, once your Shopify data resides in BigQuery, you can join it with data from other sources like advertising platforms, CRM systems, or additional Shopify stores. This enables unified analytics that connect ecommerce transactions with marketing spend and customer service interactions.
Do I need coding skills to connect Shopify to BigQuery?▼
No coding is required to establish the connection or configure your data pipelines. Kondado provides a visual interface where you authenticate your store, select pipelines, and set schedules. The platform manages the technical complexity of API connections and schema mapping automatically.
Can I track abandoned carts in BigQuery from Shopify?▼
Yes, the Abandoned Checkouts pipeline captures incomplete transactions including cart value, items left behind, and customer contact information. Analyzing this data in BigQuery helps you identify patterns in cart abandonment and calculate potential revenue recovery opportunities.
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