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Business productivity ⇄ Data warehouse

Amazon Seller Central to BigQuery integration — real-time data sync

Keep Amazon Seller Central and BigQuery 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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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Amazon Seller Central and BigQuery

Get the data locked inside Amazon Seller Central into BigQuery as live tables, and send results back where Amazon Seller Central can use them, without writing a pipeline.

Amazon Seller Central is a read-only source: Stacksync reads its data in real time and delivers it into BigQuery, so BigQuery always reflects the current state of Amazon Seller Central — without exports, scripts, or schedulers.

Whatever Amazon Seller Central is used for, it accumulates data the rest of the company wants to analyze, and that data usually sits behind an API rather than in the warehouse. Building and babysitting an extraction pipeline is the tax most teams pay for it.

Common use cases

  • Track price and inventory report changes across marketplaces from one queryable table.
  • Land Amazon orders, reports, and settlement data in your warehouse or ERP as they are produced.
  • Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • Feed ML feature tables in BigQuery from operational systems on a continuous schedule

Where Amazon Seller Central accepts updates: operational write-back

Segments, scores, or reference values computed in BigQuery sync back onto records in Amazon Seller Central, putting analysis where the work happens.

History that outlives the tool

A continuously synced copy in BigQuery preserves a queryable record even as data ages out of Amazon Seller Central or gets changed inside it.

Analytics on Amazon Seller Central's data

Records and events from Amazon Seller Central land in BigQuery as queryable tables, current within seconds and ready to join with the rest of the warehouse.

What you can sync between Amazon Seller Central and BigQuery

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.

Amazon Seller Central objects BigQuery objects
Reports Asynchronous bulk exports used for large reads (orders, inventory, settlements). Datasets Organizational container — you pick which dataset’s tables to sync.
Feeds Asynchronous bulk write channel for price, inventory, and listing updates. Projects Connection scope: the service account grants access per project.
Product Pricing Current price and competitive pricing data read for repricing analysis. Tables The syncable unit: only tables can be synced per the Stacksync docs.
Orders Order headers pulled into ERP, CRM, or database tables for fulfillment and reporting. Partitioned tables Synced like regular tables; partition columns map to target fields.
Order Items Line-level SKU, quantity, and price data joined to orders in downstream systems. Clustered tables Supported; clustering is transparent to the sync.
What ships with Amazon Seller Central ⇄ BigQuery

Connect Amazon Seller Central and BigQuery for flexible, real-time data sync.

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Seller Central or BigQuery 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 Amazon Seller Central or BigQuery record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon Seller Central and BigQuery.

How the Amazon Seller Central and BigQuery connectors work

Amazon Seller Central

Integration surface
REST API (Selling Partner API, SP-API)
Authentication
SP-API app credentials (LWA client ID/secret, application ID, Merchant ID/Seller ID token, refresh token, region) entered into the Stacksync connection form
Change detection
Historical and incremental syncs (mechanism not further specified)
Capabilities
read
Rate limits
Per-operation token-bucket rate limits; bulk reads and writes should go through the asynchronous Reports and Feeds APIs instead of item-by-item calls
Amazon Seller Central setup guide

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
How it works

How to connect Amazon Seller Central to BigQuery — 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 Amazon Seller Central and BigQuery 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
    Amazon Seller Central connected
    BigQuery connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Seller Central and BigQuery 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 · Amazon Seller Central ⇄ BigQuery
    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
    Amazon Seller Central BigQuery
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Seller Central and BigQuery integration FAQ

SECURITY

Security teams love 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.

Learn more about security
SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

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 386 integrations available for Amazon Seller Central and BigQuery.

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