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Analytics ⇄ Database

Adobeanalytics to PostgreSQL integration — real-time data sync

Keep Adobeanalytics and PostgreSQL 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 PostgreSQL

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

Adobeanalytics is a read-only source: Stacksync reads its data in real time and delivers it into PostgreSQL, so PostgreSQL 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 PostgreSQL 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 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 Consolidate data from several microservice databases into one operational Postgres store
  • 04 Feed reporting and BI from a continuously synced Postgres replica instead of scheduled ETL scripts

Common sync patterns

One version of each user or account

A user, account, or record corrected in either system updates the other, so the identity your reports group by matches the identity your database stores.

Filter and grouping dimensions kept fresh

Attributes teams slice by, such as plan, region, or account owner, stay current in Adobeanalytics because they sync from PostgreSQL as they change, instead of going stale after a one-time import.

Where Adobeanalytics tracks product events: behavior onto stored records

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

What you can sync between Adobeanalytics and PostgreSQL

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 PostgreSQL objects How this pairing syncs
Calculated Metrics User-defined derived metrics, read via GET /calculatedmetrics; mirrored so downstream tools reference the same calculated-metric definitions. JSONB Columns Hold semi-structured payloads such as nested SaaS objects or metadata. Calculated Metrics is specific to Adobeanalytics and JSONB Columns to PostgreSQL — 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. Sequences Generate surrogate keys for rows created by inbound syncs. Segments is specific to Adobeanalytics and Sequences to PostgreSQL — 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. Custom Types and Enums Constrain synced values to a fixed set, mirroring picklist fields. Date Ranges is specific to Adobeanalytics and Custom Types and Enums to PostgreSQL — 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. Tables The primary sync target; rows map one-to-one to records in connected SaaS systems. Report Suites is specific to Adobeanalytics and Tables to PostgreSQL — each maps to any object or custom field on the other side.
Users Users in the Analytics company, read via GET /users and /users/me; loaded for access, entitlement, and identity reconciliation reporting. Views Read-side projections used to expose joined or filtered data to a sync. Users is specific to Adobeanalytics and Views to PostgreSQL — each maps to any object or custom field on the other side.
Usage and Access Logs Admin audit and usage logs of report and tool activity, read via the usage/audit-log endpoints; loaded for security, governance, and adoption reporting. Materialized Views Precomputed result sets synced outward on a refresh schedule. Usage and Access Logs is specific to Adobeanalytics and Materialized Views to PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between Adobeanalytics and PostgreSQL

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

PostgreSQL Adobeanalytics Sub-second propagation

DetectionChanges in PostgreSQL are captured at the source via change data capture — no polling loop against its API. Logical replication (wal_level = logical) for change data capture via the "Postgres" connector.

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 PostgreSQL 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.
  • PostgreSQL: No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput.
What ships with Adobeanalytics ⇄ PostgreSQL

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Adobeanalytics or PostgreSQL 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 PostgreSQL record.

Observability

Monitoring

Track your Adobeanalytics ⇄ PostgreSQL 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 PostgreSQL.

How the Adobeanalytics and PostgreSQL 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.

PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL frontend/backend protocol)
Authentication
Database credentials (connection string or parameters), with optional SSL root certificate upload and optional SSH tunnel (SSH user + host); a least-privilege DB user
Change detection
Logical replication (wal_level = logical) for change data capture via the "Postgres" connector; database triggers (TRIGGER grant + stacksync_logging schema) via the trigger-based "Postgres Heroku" connector where
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput
PostgreSQL setup guide
How it works

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

    Choose tables

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

Adobeanalytics and PostgreSQL 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 476 integrations available for Adobeanalytics and PostgreSQL.

Popular · 7 of 476
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