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Data warehouse ⇄ CRM

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

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

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Databricks and Shopify

Sync Shopify into Databricks continuously and push warehouse results back onto CRM records, one two-way connection instead of two pipelines.

The CRM feeds the warehouse and the warehouse should feed the CRM: relationship data flows one way, and computed scores, segments, and customer context flow back. Most teams build the first half as a batch pipeline and never quite get to the second.

Stacksync does both with one connection. ProductVariants, Orders, Customers, Abandoned Checkouts from Shopify land in Databricks as live tables, updated within seconds, and columns computed in Databricks write back to fields in Shopify. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • Write inventory levels from a WMS or ERP into Shopify locations to keep availability accurate.
  • Mirror the product catalog from a PIM or database into Shopify products, variants, and metafields.
  • Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.

A single customer view

Join Shopify's relationship data with billing, product, and support data in Databricks to build the customer picture the CRM alone cannot hold.

Cleanup that sticks

Deduplication and normalization done in Databricks can be written back, so warehouse-side cleanup actually fixes the CRM.

CRM analytics on live data

Accounts, contacts, and activity from Shopify are queryable in Databricks moments after they change, so dashboards stop lagging the reality they describe.

What you can sync between Databricks and Shopify

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.

Databricks objects Shopify objects
Change Data Feed Row-level change records on Delta tables that drive incremental reads. Orders Purchase transactions; pushed to ERPs for fulfillment and billing, and read into databases for reporting.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Customers Buyer records; matched to CRM contacts for marketing and lifetime-value analysis.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Abandoned Checkouts Synced with incremental and full sync per the Stacksync docs.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Products Catalog entries; often mastered in a PIM or ERP and written into Shopify.
Views Curated read-only projections used as sync sources for downstream tools. ProductMedias Synced with incremental and full sync per the Stacksync docs.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. ProductVariants Synced with incremental and full sync per the Stacksync docs.
What ships with Databricks ⇄ Shopify

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Shopify 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 Databricks or Shopify record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and Shopify.

How the Databricks and Shopify connectors work

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

Shopify

Integration surface
GraphQL Admin API (primary) and REST Admin API (legacy)
Authentication
OAuth via a custom Shopify app: admin creates an app in the Shopify Dev Dashboard, enables required API scopes, sets the Stacksync redirect URL, then supplies shop name + Client ID and Client Secret to Stacksync
Change detection
Webhook topics per resource, with polling on updated_at as a fallback
Capabilities
read · write · webhooks
Rate limits
GraphQL uses a calculated query-cost budget; the REST API uses a leaky-bucket model.
Shopify setup guide
How it works

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

    Choose tables

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

Databricks and Shopify 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 Databricks and Shopify.

Popular · 8 of 386
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