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E-commerce ⇄ Database

Bigcommerce to Jdbc integration — real-time, two-way sync

Keep Bigcommerce and Jdbc 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 Bigcommerce and Jdbc

Give your engineers Bigcommerce's products, orders, and customers in Jdbc: read them with normal queries, write inventory and prices back through the sync, and skip the storefront API.

E-commerce data sits behind an API built for the storefront, not for your internal systems. Teams that need those records, for order routing, inventory logic, reporting, or back-office tools, end up writing integration code against a rate-limited API and maintaining it through every catalog change and platform upgrade.

Stacksync mirrors Products, Variants and SKUs, Orders, Customers from Bigcommerce into Jdbc and keeps both sides consistent in real time. Whatever Bigcommerce holds, whether products, orders, customers, or inventory, those records become rows your code can query, and changes written in Jdbc, such as new prices, stock levels, or fulfillment status, sync back into Bigcommerce with its validations respected.

Merchandising and operations keep working in the storefront, engineers keep working in the database, and neither side has to reconcile against a nightly export.

Common use cases

  • 01 Connect a niche or legacy RDBMS that has no dedicated Stacksync connector but ships a JDBC driver, using its JDBC URL to sync it two-way.
  • 02 Incrementally sync a high-volume table by polling an updated_at or auto-increment column, keeping a downstream store fresh without full reloads.
  • 03 Two-way sync of Products and Variants between BigCommerce and a PIM or ERP so catalog content, SKUs, and pricing stay aligned across systems.
  • 04 Write Inventory levels from a WMS or ERP onto BigCommerce Variants and SKUs so storefront availability reflects warehouse on-hand counts.

Common sync patterns

Read the store's data with a query

Records from Bigcommerce, whether products, orders, customers, or inventory, live in Jdbc as ordinary tables or collections, joinable with the rest of your data.

Internal tools and automations without API code

Scripts and services read and write the synced tables; Stacksync handles the Bigcommerce API, rate limits, and retries.

React to store changes

Updates in Bigcommerce arrive as row changes in Jdbc, so jobs and triggers can respond the moment an order, price, or stock level changes.

What you can sync between Bigcommerce and Jdbc

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.

Bigcommerce objects Jdbc objects How this pairing syncs
Products Catalog V3 records with custom fields and images; mastered in a PIM or ERP and written to BigCommerce, or read out to a warehouse. Tables The base relational tables in the target database; synced two-way as rows over SQL, with each table's primary key driving upserts and row-level updates. Products is specific to Bigcommerce and Tables to Jdbc — each maps to any object or custom field on the other side.
Variants and SKUs Per-variant pricing and inventory; synced to keep stock and SKU data aligned with an ERP or WMS at the option level. Views Stored SELECT queries exposed like tables; read-only projections synced outbound when raw base tables should not be exposed downstream. Variants and SKUs is specific to Bigcommerce and Views to Jdbc — each maps to any object or custom field on the other side.
Orders V2 Orders API header, line items, and shipping/billing addresses; read into an ERP or accounting system, with status written back. Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. Orders is specific to Bigcommerce and Columns to Jdbc — each maps to any object or custom field on the other side.
Customers V3 customer accounts, customer groups, and addresses; synced two-way with a CRM so storefront and GTM records match. Primary keys & indexes Key and index definitions read via DatabaseMetaData; the primary key is required for reliable upserts, and indexes on the cursor column keep incremental polling fast. Customers is specific to Bigcommerce and Primary keys & indexes to Jdbc — each maps to any object or custom field on the other side.
Categories Catalog V3 category tree; mapped for merchandising and kept aligned with a product master or PIM. Schemas & catalogs Namespaces that group tables and views; the connector targets a schema/catalog and lists its objects from the JDBC metadata to build the sync. Categories is specific to Bigcommerce and Schemas & catalogs to Jdbc — each maps to any object or custom field on the other side.
Brands Catalog V3 brand records linked to products; kept aligned with a product master or PIM so brand names and pages stay consistent. Stored procedures & functions Server-side routines callable via JDBC CallableStatement; invoked for custom read or write logic when a table-level mapping is not enough. Brands is specific to Bigcommerce and Stored procedures & functions to Jdbc — each maps to any object or custom field on the other side.

How changes propagate between Bigcommerce and Jdbc

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.

Bigcommerce Jdbc Sub-second propagation

DetectionBigcommerce notifies Stacksync of record changes through webhook events. Webhooks push near-real-time events (store/order/*, store/product/*, store/customer/* and more).

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

Jdbc Bigcommerce Interval-based propagation

DetectionStacksync polls Jdbc for changes on an incremental schedule, reading only records changed since the previous pass. No native change feed.

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

Rate-limit considerations

  • Bigcommerce: OAuth quota refreshes every 30 seconds and varies by plan (about 20,000 requests/hour on Standard and Plus, 60,000 on Pro, higher on Enterprise); the X-Rate-Limit-Requests-Left header reports remaining calls.
  • Jdbc: No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
What ships with Bigcommerce ⇄ Jdbc

Connect Bigcommerce and Jdbc for flexible, real-time data sync.

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

Real-time

Two-way sync

Changes in Bigcommerce or Jdbc instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Bigcommerce or Jdbc 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 Bigcommerce or Jdbc record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Bigcommerce and Jdbc.

How the Bigcommerce and Jdbc connectors work

Bigcommerce

Integration surface
REST Management API (V2 and V3), plus GraphQL Storefront and Admin APIs
Authentication
OAuth API account credentials — a store-generated client ID and permanent access token sent in the X-Auth-Token header, limited to the OAuth scopes granted when the account is created (e.g. store_v2_orders, store_v2_products)
Change detection
Webhooks push near-real-time events (store/order/*, store/product/*, store/customer/* and more); polling uses date_modified:min/max filters on Products, Orders, and Customers
Capabilities
read · write · webhooks
Rate limits
OAuth quota refreshes every 30 seconds and varies by plan (about 20,000 requests/hour on Standard and Plus, 60,000 on Pro, higher on Enterprise); the X-Rate-Limit-Requests-Left header reports remaining calls.
Bigcommerce setup guide

Jdbc

Integration surface
JDBC API (java.sql / javax.sql) executing SQL through a JDBC driver, typically a pure-Java Type 4 driver; reaches any relational database with a driver - PostgreSQL, MySQL, SQL Server, Oracle, IBM DB2, and others - via a JDBC URL such as jdbc:postgresql://host:5432/db.
Authentication
A database user's username and password supplied in the JDBC connection (DriverManager or a DataSource), typically over a TLS/SSL-encrypted connection. Some drivers add Kerberos, integrated Windows auth, or cloud IAM-token auth, but the available methods depend on the target database and its driver.
Change detection
No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks.
Capabilities
read · write
Rate limits
No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
How it works

How to connect Bigcommerce to Jdbc — 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 Bigcommerce and Jdbc 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
    Bigcommerce connected
    Jdbc connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Bigcommerce and Jdbc 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 · Bigcommerce ⇄ Jdbc
    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
    Bigcommerce Jdbc
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Bigcommerce and Jdbc 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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→ 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 389 integrations available for Bigcommerce and Jdbc.

Popular · 7 of 389
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