Shopify to BigQuery, analytics-ready
Sync Shopify data into BigQuery with a standardized schema, documented metrics, and verifiable attribution.
Warehouse-first: your Shopify data lands in BigQuery tables your team can query directly.
Warehouse path
Shopify
modeled and standardized
Looking for the general connector page? View Shopify integration.
Why teams search for Shopify to BigQuery
Common pain points this page is built to solve.
Numbers don't reconcile
Shopify reports often conflict with ad platform reporting, slowing channel decisions.
CSV workflows break
Manual exports become brittle as order volume and reporting complexity increase.
Need queryable warehouse data
Teams need direct SQL access instead of fixed dashboard abstractions.
Auditability matters
Black-box logic makes it harder to defend decisions in budget and performance reviews.
What you get
A governed Shopify data foundation that is ready for analysis and activation.
Analytics-ready tables
Orders, customers, products, and transaction context are modeled for BI use, not raw-dump cleanup work.
Standardized ecommerce schema
Consistent naming and stable join keys across SourceMedium integrations reduce ad-hoc reconciliation work.
Documented metrics
Metric definitions and lineage are documented so teams can verify numbers in BigQuery, not trust opaque calculations.
Attribution-ready joins
Shopify revenue joins with session and spend context for channel analysis. See attribution use case.
What is different vs. standard connectors?
Connector-only
Moves raw rows from source to destination. Modeling, definitions, and reconciliation are left to your team.
SourceMedium foundation
Delivers a modeled warehouse layer with documentation, standardized definitions, and attribution-ready joins your team can verify directly.
Dedicated BigQuery capacity for BI and transformations
We reserve BigQuery compute so dashboards and transformation workloads do not compete for the same shared resources. Performance stays consistent, and capacity scales as your workload grows.
Learn more about the underlying warehouse model on the BigQuery page.
Note
BigQuery reserved capacity (slots) is managed by SourceMedium; your team gets stable query performance without warehouse-sizing operations overhead.
Example Shopify models in BigQuery
Representative tables from the existing Shopify integration model.
-
obt_orders
Order-level facts with standardized revenue, source, and order status context.
-
obt_order_lines
Product-level line items with SKU, quantity, and price context.
-
obt_customers
Customer-level records with stable identifiers and order history context.
-
dim_orders
Order dimension table with durable keys for repeatable joins.
-
dim_product_variants
Product variant dimension with SKU and attribute normalization.
Example query
-- revenue and new-customer rate by channel (last 30 days)
SELECT
sm_channel,
SUM(order_net_revenue) AS revenue,
AVG(CASE WHEN is_first_order THEN 1 ELSE 0 END) AS new_customer_rate
FROM dataset.obt_orders
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY 1
ORDER BY revenue DESC;Joins with
Use Shopify models with your core paid and lifecycle channels.
How it works
A simple three-step implementation flow.
Step 1
Connect Shopify
Authorize read-only access to Shopify so SourceMedium can ingest orders, customers, products, and transactions.
Step 2
Backfill and incremental sync
Historical data is reconciled first, then frequent incremental updates keep the warehouse current.
Step 3
Query in BigQuery or ask AI
Use SQL directly in BigQuery or ask questions in natural language through SourceMedium AI Analyst.
FAQ
Answers to the most common implementation and ownership questions.
Do we keep the data if we leave?
Yes. Your data lives in BigQuery and remains usable by your team.
Is this just a raw export?
No. SourceMedium provides modeled schema, documented metrics, and consistent joins for analysis.
How do I export Shopify data to BigQuery?
SourceMedium syncs Shopify into modeled BigQuery tables, so your team queries analytics-ready schema directly instead of managing manual CSV exports.
How does billing work?
Platform pricing is fixed, while BigQuery usage scales with workload. See the pricing page for details. View pricing.
Is AI included?
Yes. AI Analyst is included so teams can ask questions in natural language and verify outputs in SQL.
How fresh is the data?
Sync cadence is configurable. Most teams run frequent incremental updates after initial backfill.
Get Shopify data you can actually trust
Use SourceMedium to unify Shopify data with the rest of your ecommerce stack on a BigQuery foundation your team can verify.
Ready to stop debating the numbers?
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