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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.

Teams sync Amazon Seller Central into BigQuery to make marketplace performance part of the analytics warehouse. Orders and Financial Events flow into BigQuery datasets where partitioned tables keep large order histories cheap to scan and easy to join with the rest of the business.

Common use cases

  • 01 Blend Amazon Shipments data with warehouse-wide logistics tables in one BigQuery project.
  • 02 Analyze fee and settlement trends from Financial Events without exporting reports manually.
  • 03 Track Listings / Catalog Items changes over time using partitioned tables.
  • 04 Track price and inventory report changes across marketplaces from one queryable table.

Common sync patterns

Order analytics pipeline

Orders and Order Items load into partitioned BigQuery tables keyed by purchase date for time-series analysis.

Inventory snapshots

FBA Inventory syncs into clustered tables so per-SKU stock trends can be queried efficiently.

Financial reporting feed

Financial Events populate a BigQuery dataset used for revenue and fee reporting.

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 How this pairing syncs
Reports Asynchronous bulk exports used for large reads (orders, inventory, settlements). Datasets Organizational container — you pick which dataset’s tables to sync. Reports is specific to Amazon Seller Central and Datasets to BigQuery — each maps to any object or custom field on the other side.
Feeds Asynchronous bulk write channel for price, inventory, and listing updates. Projects Connection scope: the service account grants access per project. Feeds is specific to Amazon Seller Central and Projects to BigQuery — each maps to any object or custom field on the other side.
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. Product Pricing is specific to Amazon Seller Central and Tables to BigQuery — each maps to any object or custom field on the other side.
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. Orders is specific to Amazon Seller Central and Partitioned tables to BigQuery — each maps to any object or custom field on the other side.
Order Items Line-level SKU, quantity, and price data joined to orders in downstream systems. Clustered tables Supported; clustering is transparent to the sync. Order Items is specific to Amazon Seller Central and Clustered tables to BigQuery — each maps to any object or custom field on the other side.

How changes propagate between Amazon Seller Central and BigQuery

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.

Amazon Seller Central BigQuery Interval-based propagation

DetectionStacksync polls Amazon Seller Central for changes on an incremental schedule, reading only records changed since the previous pass. Historical and incremental syncs (mechanism not further specified).

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

BigQuery Amazon Seller Central 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").

DeliveryAmazon Seller Central does not accept inbound record writes, so this direction carries requests rather than records: Amazon Seller Central's output flows back as field updates on the originating BigQuery records.

Rate-limit considerations

  • Amazon Seller Central: 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.
  • 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.
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 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.

Learn more about security
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→ 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 504 integrations available for Amazon Seller Central and BigQuery.

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