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Analytics ⇄ Data warehouse

Adobeanalytics to Databricks integration — real-time data sync

Keep Adobeanalytics 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 Adobeanalytics and Databricks

Flow Adobeanalytics data into Databricks in real time — no exports, no schedulers, no custom scripts.

Adobeanalytics is a read-only source: Stacksync reads its data in real time and delivers it into Databricks, so Databricks always reflects the current state of Adobeanalytics — without exports, scripts, or schedulers.

Adobeanalytics is where teams explore, visualize, and report; Databricks is the store of record that holds the raw tables and full history behind those views. The two overlap wherever the same events, users, and metrics matter to both, and when the bridge between them is a nightly export or a hand-built extract, dashboards lag the warehouse and analysts spend the morning arguing over whose number is right.

Common use cases

  • 01 Read Users and Usage/Audit logs into a security or governance database for access reviews and tool-usage reporting across the Analytics company.
  • 02 Feed report metrics into a data model that blends channel performance with conversion and cost data.
  • 03 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 04 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.

Common sync patterns

Shared user and account keys

Users and accounts tracked in Adobeanalytics line up with the customer or user rows in Databricks on a stable key, so both sides count the same population.

Corrections propagate instead of reloading

When a record is fixed or backfilled on one side, the change reaches the other without a full reload, keeping history consistent across both.

One number both sides agree on

Metrics and aggregates stay aligned between the two systems, so a figure shown in Adobeanalytics matches the Databricks table it was built from instead of drifting between refreshes.

What you can sync between Adobeanalytics 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.

Adobeanalytics objects Databricks objects How this pairing syncs
Dimensions Available report dimensions (eVars, props, and standard dimensions) per report suite, read via GET /dimensions; pulled as reporting metadata to build report requests and mirror the model. Volumes Unity Catalog file storage used for staging bulk loads. Dimensions is specific to Adobeanalytics and Volumes to Databricks — each maps to any object or custom field on the other side.
Metrics Standard metrics available for a report suite, read via GET /metrics; read as metadata to construct report requests and document available measures. SQL Warehouses The compute endpoint a sync connects to for query execution. Metrics is specific to Adobeanalytics and SQL Warehouses to Databricks — each maps to any object or custom field on the other side.
Calculated Metrics User-defined derived metrics, read via GET /calculatedmetrics; mirrored so downstream tools reference the same calculated-metric definitions. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Calculated Metrics is specific to Adobeanalytics and Change Data Feed to Databricks — each maps to any object or custom field on the other side.
Segments Saved segment definitions used to filter reports, read via GET /segments; cataloged and reused so downstream systems target the same audiences. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Segments is specific to Adobeanalytics and Catalogs to Databricks — each maps to any object or custom field on the other side.
Date Ranges Saved relative or rolling date ranges, read via GET /dateranges; read as reusable reporting components for report requests. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Date Ranges is specific to Adobeanalytics and Schemas to Databricks — each maps to any object or custom field on the other side.
Report Suites Report suite and virtual report suite configuration read via the /collections/suites endpoint; enumerated to list the report suites available to the company. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Report Suites is specific to Adobeanalytics and Delta Tables to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Adobeanalytics 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.

Adobeanalytics Databricks Interval-based propagation

DetectionStacksync polls Adobeanalytics for changes on an incremental schedule, reading only records changed since the previous pass. Pull-based over a date range: reports are requested for a from/to window and re-queried on a schedule.

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

Databricks Adobeanalytics 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.

DeliveryAdobeanalytics does not accept inbound record writes, so this direction carries requests rather than records: Adobeanalytics's output flows back as field updates on the originating Databricks records.

Rate-limit considerations

  • Adobeanalytics: The Analytics 2.0 API enforces 12 requests per 6 seconds (about 120 per minute) per user; exceeding it returns HTTP 429 with error_code 429050. A separate per-report-suite reporting-engine throttle can slow large requests without returning an error.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Adobeanalytics ⇄ Databricks

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Adobeanalytics 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 Adobeanalytics or Databricks record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Adobeanalytics and Databricks connectors work

Adobeanalytics

Integration surface
Analytics 2.0 REST API on analytics.adobe.io for reporting and components; Data Feeds and Data Warehouse for raw hit-level export; Data Insertion and Bulk Data Insertion (CSV) APIs for inbound server-side collection.
Authentication
OAuth Server-to-Server via the Adobe Developer Console (JWT service-account auth was deprecated January 1, 2025). The company's global company ID is sent in the x-proxy-global-company-id header, and the integration needs at least the Report Suites, Metrics, and Dimensions permission groups.
Change detection
Pull-based over a date range: reports are requested for a from/to window and re-queried on a schedule. No change-data-capture feed or report-data webhooks; Adobe recommends not polling for new data faster than every 30 minutes and caching results.
Capabilities
read
Rate limits
The Analytics 2.0 API enforces 12 requests per 6 seconds (about 120 per minute) per user; exceeding it returns HTTP 429 with error_code 429050. A separate per-report-suite reporting-engine throttle can slow large requests without returning an error.

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

    Choose tables

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

Adobeanalytics 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 481 integrations available for Adobeanalytics and Databricks.

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