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E-commerce ⇄ Data warehouse

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

Keep Akeneo 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 Akeneo and Databricks

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

Databricks is the analytical store where the business joins, models, and reports on its data; Akeneo 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 Volumes, SQL Warehouses, Change Data Feed, Catalogs in Databricks with Attributes, Families and family variants, Reference entities and records, Assets in Akeneo 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 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 02 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 03 Write Categories, Families, and Attributes from an ERP or supplier feed into Akeneo to build and maintain catalog structure.
  • 04 Read Reference entities records such as brands and manufacturers into a data warehouse for cross-catalog reporting.

Common sync patterns

Where Akeneo accepts writes: push computed attributes back

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

Inventory and order status reconciled

Stock levels and order or fulfillment status move between Databricks and Akeneo so counts and states agree across reporting and operations.

What you can sync between Akeneo 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.

Akeneo objects Databricks objects How this pairing syncs
Products Core catalog records addressed by UUID (recommended) or identifier/SKU; synced two-way with databases, ERPs, and storefronts. Values are scoped per channel and locale. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Products is specific to Akeneo and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Product models Parent records for configurable products; hold shared attribute values that cascade to their variant products, synced alongside Products. Views Curated read-only projections used as sync sources for downstream tools. Product models is specific to Akeneo and Views to Databricks — each maps to any object or custom field on the other side.
Categories The category tree used to classify products; read out for storefront navigation or written in from an ERP to seed catalog structure. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Categories is specific to Akeneo and Materialized Views to Databricks — each maps to any object or custom field on the other side.
Attributes Attribute definitions plus attribute options and groups; describe the shape of product data, usually mastered in Akeneo and read downstream. Volumes Unity Catalog file storage used for staging bulk loads. Attributes is specific to Akeneo and Volumes to Databricks — each maps to any object or custom field on the other side.
Families and family variants Templates defining which attributes a product carries and how variants axis out; synced to keep catalog structure consistent across systems. SQL Warehouses The compute endpoint a sync connects to for query execution. Families and family variants is specific to Akeneo and SQL Warehouses to Databricks — each maps to any object or custom field on the other side.
Reference entities and records Structured lookups like brands, manufacturers, or ingredients with their own records (Enterprise Edition); synced two-way or read into a warehouse for reporting. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Reference entities and records is specific to Akeneo and Change Data Feed to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Akeneo 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.

Akeneo Databricks Sub-second propagation

DetectionAkeneo notifies Stacksync of record changes through webhook events. Events API webhooks (product.created, product.updated, product.deleted, plus product-model equivalents.

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

Databricks Akeneo 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 Akeneo through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Akeneo: REST protection triggers above roughly 100 requests/second per PIM instance (HTTP 429 with Retry-After); the Events API caps at 4000 requests/hour with a 500ms webhook ack timeout.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Akeneo ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Akeneo 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 Akeneo or Databricks record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Akeneo and Databricks connectors work

Akeneo

Integration surface
REST API (plus a GraphQL API) and an Events API for webhooks
Authentication
OAuth2 password grant: client_id/secret plus an API user's username/password exchanged at /api/oauth/v1/token for a bearer access_token and refresh_token; Apps use the OAuth2 authorization-code flow
Change detection
Events API webhooks (product.created, product.updated, product.deleted, plus product-model equivalents; product.updated.delta carries old and new values); falls back to polling the 'updated' datetime search filter where the Events API is unavailable
Capabilities
read · write · webhooks
Rate limits
REST protection triggers above roughly 100 requests/second per PIM instance (HTTP 429 with Retry-After); the Events API caps at 4000 requests/hour with a 500ms webhook ack timeout.

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 Akeneo 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 Akeneo 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
    Akeneo connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Akeneo 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 Akeneo and Databricks.

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