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Analytics ⇄ Database

Adobeanalytics to AWS Aurora PostgreSQL integration — real-time data sync

Keep Adobeanalytics and AWS Aurora 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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Why teams connect Adobeanalytics and AWS Aurora PostgreSQL

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

Adobeanalytics is a read-only source: Stacksync reads its data in real time and delivers it into AWS Aurora PostgreSQL, so AWS Aurora 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 AWS Aurora 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 Pull ranked and trended Reports (metrics by dimension) into Postgres or a warehouse so web and marketing analytics join with CRM, ad-spend, and revenue data in SQL.
  • 02 Mirror Dimensions, Metrics, Calculated Metrics, and Segment definitions into a catalog database to document the Adobe Analytics reporting model.
  • 03 Keep a customer-facing Aurora database aligned with an internal admin tool, with writes accepted on both sides.
  • 04 Feed operational dashboards from a read replica while the writer handles sync traffic.

Common sync patterns

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

Segments, cohorts, or scores computed in Adobeanalytics sync back into AWS Aurora PostgreSQL, 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 AWS Aurora PostgreSQL land in Adobeanalytics as they change, so dashboards, funnels, and metrics run on current data instead of last night's extract.

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.

What you can sync between Adobeanalytics and AWS Aurora 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 AWS Aurora PostgreSQL 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. Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. Reports is specific to Adobeanalytics and Primary keys and constraints to AWS Aurora PostgreSQL — 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. Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. Dimensions is specific to Adobeanalytics and Views and materialized views to AWS Aurora PostgreSQL — 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. Foreign keys Relationship metadata that syncs can translate into object references elsewhere. Metrics is specific to Adobeanalytics and Foreign keys to AWS Aurora PostgreSQL — 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. Replication slots and publications The logical replication objects that power log-based CDC. Calculated Metrics is specific to Adobeanalytics and Replication slots and publications to AWS Aurora 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. Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. Segments is specific to Adobeanalytics and Databases and schemas to AWS Aurora 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. Tables The core sync unit; rows are matched across systems by primary key. Date Ranges is specific to Adobeanalytics and Tables to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between Adobeanalytics and AWS Aurora 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 AWS Aurora 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 AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.

AWS Aurora PostgreSQL Adobeanalytics Sub-second propagation

DetectionChanges in AWS Aurora PostgreSQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback.

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 AWS Aurora 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.
What ships with Adobeanalytics ⇄ AWS Aurora PostgreSQL

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Adobeanalytics or AWS Aurora 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 AWS Aurora PostgreSQL record.

Observability

Monitoring

Track your Adobeanalytics ⇄ AWS Aurora 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 AWS Aurora PostgreSQL.

How the Adobeanalytics and AWS Aurora 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.

AWS Aurora PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
Capabilities
read · write · CDC
How it works

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

    Choose tables

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

Adobeanalytics and AWS Aurora PostgreSQL integration FAQ

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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 383 integrations available for Adobeanalytics and AWS Aurora PostgreSQL.

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