Source connector

BigCommerce connector

BigCommerce integration for databases, warehouses and BI tools

Ecommerce platform for catalog, orders, and checkout. Datrise captures BigCommerce entities such as orders, products, customers, carts, transactions and categories, then lands normalized and typed models for warehouse reporting, attribution, and AI workflows.

The BigCommerce REST Management API v3 (Catalog, Orders, Customers) authenticates with API key, OAuth 2.0, incremental runs key on date_modified:min (query filter, e.g. GET /catalog/products?date_modified:min=2026-01-01T00:00:00), and BigCommerce caps calls at 150 requests per 30 seconds per store by default on the Pro plan (lower tiers get fewer), shared across every app calling that store and reported via the X-Rate-Limit-Requests-Left and X-Rate-Limit-Time-Reset-Ms response headers.

Of the 6 objects, 5 sync as upserts keyed on the primary key and 1 as append-only streams, so each incremental run moves only what changed since the last watermark. Every object lands in its own table (for example `bigcommerce_orders`) with a schema derived from the BigCommerce API rather than a flattened CSV, in any of the 43 supported destinations.

Datrise is in early access: pick a destination below to open the BigCommerce pipeline page, and join the waitlist to get the BigCommerce connector with guided onboarding.

Incremental syncSchema-aware mapping43 destinations available

What BigCommerce data Datrise extracts

Each object lands in its own typed table. Sync mode is derived from the object: history-style objects append, everything else upserts.

Object	Landing table	Sync mode
orders	bigcommerce_orders	Upsert by primary key, incremental on updated-at
products	bigcommerce_products	Upsert by primary key, incremental on updated-at
customers	bigcommerce_customers	Upsert by primary key, incremental on updated-at
carts	bigcommerce_carts	Upsert by primary key, incremental on updated-at
transactions	bigcommerce_transactions	Append-only, incremental by timestamp
categories	bigcommerce_categories	Upsert by primary key, incremental on updated-at

BigCommerce API: setup facts for the connector

Verified against the vendor documentation on 2026-09-02. Unknown values are left blank rather than guessed.

API	BigCommerce REST Management API v3 (Catalog, Orders, Customers)
Authentication	API key, OAuth 2.0 — Store-issued API accounts authenticate with a static X-Auth-Token header; marketplace apps instead run the full OAuth2 install flow to obtain a per-store token.
Pagination	page — List endpoints use page and limit query params, with meta.pagination.total_pages indicating when the last page has been reached.
Rate limit	150 requests per 30 seconds per store by default on the Pro plan (lower tiers get fewer), shared across every app calling that store and reported via the X-Rate-Limit-Requests-Left and X-Rate-Limit-Time-Reset-Ms response headers
Incremental cursor	date_modified:min (query filter, e.g. GET /catalog/products?date_modified:min=2026-01-01T00:00:00) — date_modified:min is supported on most catalog/order list endpoints but the exact filter name varies slightly by resource (e.g. min_date_modified on some legacy v2-only endpoints).
Webhooks	Yes — Webhooks v3 (e.g. store/product/updated, store/product/deleted, store/order/statusUpdated)
Bulk export	No
Deletes detectable	Yes — store/{resource}/deleted webhook events (e.g. store/product/deleted) fire on hard delete; the payload carries only the resource id, and a follow-up GET on that id returns 404

Official API reference

What bites when replicating BigCommerce

The default quota is 150 requests per 30 seconds per store on the Pro plan (lower on Standard/Plus), and it is shared across every app hitting that store, so a second integration installed on the same store can starve your sync's quota.
Webhook payloads are notification-only: a `store/product/updated` event carries just the scope and the product id, so every webhook still requires a follow-up GET to fetch the changed fields.
`date_modified:min` filtering is only reliable on the newer v3 endpoints; some resources still route through v2-style filters like `min_date_modified`, so the exact parameter name must be checked per resource.
Detecting deletes without webhooks means diffing full ID lists, since `date_modified:min` on a list endpoint will never surface a record that no longer exists.

Compare this against every other documented API

Popular destinations for BigCommerce

Choose a destination below to open the ready pipeline page.

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Airtable
Relational spreadsheet destination for ops and go-to-market teams.
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Amazon Athena
Serverless SQL over S3 data lake tables.
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Amazon DynamoDB
Serverless key-value and document store on AWS.
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Amazon QuickSight
AWS serverless BI with SPICE and embedded analytics.
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Amazon Redshift
AWS petabyte-scale warehouse with Spectrum.
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Amazon S3 Data Lake
Object storage landing zone for parquet and snapshots.
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Apache Superset
Open-source BI for SQL exploration, charts, and dashboard publishing.
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Azure Data Lake Storage
ADLS Gen2 object storage for analytics workloads.
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Azure Synapse
Microsoft analytics workspace with SQL pools.
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BigCommerce connector FAQ

What BigCommerce data does the connector extract?: 6 objects: orders, products, customers, carts, transactions and categories. Each lands in its own table with types preserved; history-style objects (1: transactions) load as append-only streams.
Is the BigCommerce integration incremental?: Yes. After the initial full load, every run reads only records changed since the last watermark (updated-at or event timestamp) and applies an upsert or an append depending on the object.
Where can I load BigCommerce data?: Into 43 destinations: relational databases, cloud warehouses, data lakes, spreadsheets and BI tools. Each BigCommerce → destination pair has its own page with the object-to-table mapping.

Related sources

Other sources in the same category, each with its own connector page.

Early access

Launch your BigCommerce pipeline with guided onboarding

Join the waitlist to get priority access for BigCommerce integrations with guided setup, schema-aware mapping, and production-grade incremental sync.