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E-commerce ⇄ Data warehouse

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

Keep Bigcommerce and Databricks 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 Databricks

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

Databricks is the analytical store where the business joins, models, and reports on its data; Bigcommerce 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 Change Data Feed, Catalogs, Schemas, Delta Tables in Databricks with Products, Variants and SKUs, Orders, Customers in Bigcommerce 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 Delta tables continuously so lakehouse models work from current operational data.
  • 02 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 03 Write Inventory levels from a WMS or ERP onto BigCommerce Variants and SKUs so storefront availability reflects warehouse on-hand counts.
  • 04 Sync Customers and customer groups two-way with a CRM so marketing and support see the same accounts and addresses as the storefront.

Common sync patterns

Live analytics on store activity

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

Where Bigcommerce accepts writes: push computed attributes back

Segments, lifetime value, and scores built in Databricks write onto the matching records in Bigcommerce, 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 Bigcommerce and Databricks

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 Databricks 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. Volumes Unity Catalog file storage used for staging bulk loads. Products is specific to Bigcommerce and Volumes to Databricks — 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. SQL Warehouses The compute endpoint a sync connects to for query execution. Variants and SKUs is specific to Bigcommerce and SQL Warehouses to Databricks — 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. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Orders is specific to Bigcommerce and Change Data Feed to Databricks — 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. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Customers is specific to Bigcommerce and Catalogs to Databricks — 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 Group tables and views; syncs typically target a dedicated schema per source system. Categories is specific to Bigcommerce and Schemas to Databricks — 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. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Brands is specific to Bigcommerce and Delta Tables to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Bigcommerce and Databricks

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 Databricks 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 Databricks as a row-level write, with types converted between the two schemas.

Databricks Bigcommerce Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

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.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Bigcommerce ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Bigcommerce or Databricks 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 Databricks record.

Observability

Monitoring

Track your Bigcommerce ⇄ Databricks 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 Databricks.

How the Bigcommerce and Databricks 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

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits
How it works

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

    Choose tables

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

Bigcommerce and Databricks 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 508 integrations available for Bigcommerce and Databricks.

Popular · 7 of 508
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