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Data warehouse ⇄ E-commerce

BigQuery to Channelengine integration — real-time, two-way sync

Keep BigQuery and Channelengine in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Why teams connect BigQuery and Channelengine

Make BigQuery the analytics and enrichment layer behind Channelengine: orders, products, and customers stay current in both directions.

BigQuery is the analytical store where the business joins, models, and reports on its data; Channelengine runs the storefront, catalog, and transactions that generate most of it. The overlap is every record that has to be counted and enriched on one side and acted on the other — and when the bridge is a nightly export, the warehouse reports on yesterday while the store operates without the segments and metrics the warehouse just computed.

Stacksync syncs Projects, Tables, Partitioned tables, Clustered tables in BigQuery with Cancellations, Backorders, Order documents, Orders in Channelengine field by field, in real time, and in both directions. Transactional records land in the warehouse as they change, computed attributes and cleaned catalog data flow back to the store, and you decide which system owns which fields so Stacksync resolves conflicts by rules you set.

Common use cases

  • 01 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
  • 02 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
  • 03 Sync Returns and Cancellations two-way so customer-service and finance systems reconcile marketplace returns and cancellations with the ERP.
  • 04 Load Orders and Returns into Postgres or a warehouse for marketplace sales, fulfillment, and margin reporting without manual CSV exports.

Common sync patterns

Live analytics on store activity

Orders, products, and customer records from Channelengine land in BigQuery as they change, so dashboards and models read current data instead of last night's export.

Where Channelengine accepts writes: push computed attributes back

Segments, lifetime value, and scores built in BigQuery write onto the matching records in Channelengine, so merchandising and messaging act on warehouse logic.

One product catalog

Where both systems hold product or catalog data, cleaned and enriched attributes stay aligned so the store and the warehouse describe the same items.

What you can sync between BigQuery and Channelengine

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

BigQuery objects Channelengine objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. Shipments Fulfillment records; write with POST /v2/shipments to mark an order shipped with tracking, read with GET /v2/shipments. Pushed back from a warehouse or ERP so marketplaces update the buyer. Tables is specific to BigQuery and Shipments to Channelengine — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Returns Marketplace- and merchant-initiated returns; read via GET /v2/returns and created with POST /v2/returns. Synced two-way so customer-service and finance systems reconcile with the ERP. Partitioned tables is specific to BigQuery and Returns to Channelengine — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Cancellations Order cancellations; create with POST /v2/cancellations and read with GET /v2/cancellations. Written when stock is unavailable and read back for reconciliation. Clustered tables is specific to BigQuery and Cancellations to Channelengine — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Backorders Records marking part of an order as temporarily out of stock; create with POST /v2/backorders and read with GET /v2/backorders by merchant reference or since a date. Datasets is specific to BigQuery and Backorders to Channelengine — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Order documents Invoices and other order documents; retrieved as a paginated, filterable list via GET /v2/orders/documents for finance and archiving systems. Read-only. Projects is specific to BigQuery and Order documents to Channelengine — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Channelengine

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

BigQuery Channelengine Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

DeliveryEach detected change is written to Channelengine through its API, with automatic retries and rate-limit backoff.

Channelengine BigQuery Sub-second propagation

DetectionChannelengine notifies Stacksync of record changes through webhook events. Webhooks fire on order creation and on return and shipment/cancellation changes.

DeliveryEach detected change is applied to BigQuery as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • Channelengine: Rate limits are per endpoint and returned in headers: x-rate-limit-limit (interval length in minutes), x-rate-limit-remaining, and retry-after (seconds to wait); e.g. POST /v2/supportorder allows 3 calls per minute.
What ships with BigQuery ⇄ Channelengine

Connect BigQuery and Channelengine for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Channelengine connection.

Real-time

Two-way sync

Changes in BigQuery or Channelengine instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Channelengine data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single BigQuery or Channelengine record.

Observability

Monitoring

Track your BigQuery ⇄ Channelengine sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BigQuery and Channelengine.

How the BigQuery and Channelengine connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

Channelengine

Integration surface
REST API (Merchant API v2); per-tenant base URL https://{tenant}.channelengine.net/api
Authentication
API key passed as the 'apikey' parameter; ChannelEngine recommends sending it in a request header rather than the URL because some webservers log full URLs
Change detection
Webhooks fire on order creation and on return and shipment/cancellation changes; product content and offers use change-tracking endpoints (GET /v2/products/data and /v2/products/offers) with an acknowledge pattern that returns only changed records, plus polling GET /v2/orders/new
Capabilities
read · write · webhooks
Rate limits
Rate limits are per endpoint and returned in headers: x-rate-limit-limit (interval length in minutes), x-rate-limit-remaining, and retry-after (seconds to wait); e.g. POST /v2/supportorder allows 3 calls per minute.
How it works

How to connect BigQuery to Channelengine — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate BigQuery and Channelengine with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    BigQuery connected
    Channelengine connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the BigQuery and Channelengine objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · BigQuery ⇄ Channelengine
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    BigQuery Channelengine
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

BigQuery and Channelengine integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 405 integrations available for BigQuery and Channelengine.

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