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Analytics ⇄ Database

Adobeanalytics to Citus integration — real-time data sync

Keep Adobeanalytics and Citus 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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Why teams connect Adobeanalytics and Citus

Give Adobeanalytics the users, events, and records that live in Citus in real time, and sync the cohorts and scores Adobeanalytics computes back into Citus where your applications read them.

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

A database holds the rows your business runs on: the users, events, orders, and records that every service reads and writes. Adobeanalytics is where people make sense of them, as dashboards, funnels, cohorts, and metrics. Moving the data from Citus into Adobeanalytics usually means a hand-built extract or a change-data-capture pipeline that breaks the moment a column is renamed, and reporting that always trails last night's load.

Common use cases

  • 01 Load report data on a schedule into a reporting database for executive dashboards without manual Analysis Workspace exports or Report Builder pulls.
  • 02 Sync Segment definitions into a marketing database so downstream tools target the same audiences Adobe Analytics computes.
  • 03 Consolidate per-tenant rows from distributed tables into per-customer reporting databases.
  • 04 Sync high-volume event or tenant data from a Citus cluster into a warehouse for cross-tenant analytics.

Common sync patterns

Where Adobeanalytics tracks product events: behavior onto stored records

Signup, usage, and lifecycle events captured in Adobeanalytics sync into Citus as rows, so applications and internal tools can read behavioral data next to the records they already keep.

Where Adobeanalytics builds cohorts or scores: results your services can read

Segments, cohorts, or scores computed in Adobeanalytics sync back into Citus, where the services that read from the database act on them at query speed without calling the analytics API.

Analytics on live operational data, minus the pipeline

The users, events, orders, and records stored in Citus land in Adobeanalytics as they change, so dashboards, funnels, and metrics run on current data instead of last night's extract.

What you can sync between Adobeanalytics and Citus

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 Citus objects How this pairing syncs
Reports Core reporting endpoint (POST /reports on analytics.adobe.io); returns ranked or trended report data for chosen metrics broken down by dimensions over a date range, with optional segment filters. The main dataset Stacksync reads out. Views Curated projections over distributed data, often used as read-only sync sources. Reports is specific to Adobeanalytics and Views to Citus — each maps to any object or custom field on the other side.
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. Sequences Key generators that matter when external writes must not collide with application inserts. Dimensions is specific to Adobeanalytics and Sequences to Citus — 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. Distributed tables Tables sharded across worker nodes by a distribution column; the main sync target for large datasets. Metrics is specific to Adobeanalytics and Distributed tables to Citus — 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. Reference tables Small lookup tables replicated to every node, synced like ordinary Postgres tables. Calculated Metrics is specific to Adobeanalytics and Reference tables to Citus — 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. Local tables Coordinator-only tables that behave exactly like standard PostgreSQL tables. Segments is specific to Adobeanalytics and Local tables to Citus — 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 Standard Postgres namespaces used to scope what a sync user can read and write. Date Ranges is specific to Adobeanalytics and Schemas to Citus — each maps to any object or custom field on the other side.

How changes propagate between Adobeanalytics and Citus

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

Citus Adobeanalytics Sub-second propagation

DetectionChanges in Citus are captured at the source via change data capture — no polling loop against its API. PostgreSQL logical decoding / CDC, with caveats: changes to distributed tables occur on worker shards, so CDC setup differs from single-node Postgres.

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 Citus 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.
What ships with Adobeanalytics ⇄ Citus

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Adobeanalytics or Citus 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 Citus record.

Observability

Monitoring

Track your Adobeanalytics ⇄ Citus 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 Citus.

How the Adobeanalytics and Citus 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.

Citus

Integration surface
PostgreSQL wire protocol; any standard Postgres driver connects to the coordinator node
Authentication
Database credentials (standard PostgreSQL authentication; managed deployments add cloud IAM options)
Change detection
PostgreSQL logical decoding / CDC, with caveats: changes to distributed tables occur on worker shards, so CDC setup differs from single-node Postgres
Capabilities
read · write · CDC
How it works

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

    Choose tables

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

Adobeanalytics and Citus 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 363 integrations available for Adobeanalytics and Citus.

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