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Business productivity ⇄ Database

Atlassian to PostgreSQL integration — real-time, two-way sync

Keep Atlassian 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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  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Atlassian and PostgreSQL

Mirror Atlassian's data into PostgreSQL so your own code can read and write it like any other table, with changes flowing both ways in seconds.

Engineering and platform teams connect Atlassian to PostgreSQL to keep an operational mirror of project work in the database that powers their internal tools. Syncing Jira Issues and Issue Comments into PostgreSQL Tables lets teams query delivery data with SQL, join it against product data, and build reporting Views without hitting Jira API rate limits.

Stacksync mirrors Attachments, Custom Fields, Workflows and Statuses, Users and Groups from Atlassian into JSONB Columns, Sequences, Custom Types and Enums, Tables in PostgreSQL and keeps both sides in sync in real time. Your services query the database directly, and inserts or updates your code makes flow back into Atlassian, so the tool and the database never disagree.

Common use cases

  • 01 Query Jira Projects and Issues with SQL instead of paginating the Jira API.
  • 02 Feed internal dashboards from Materialized Views built on synced sprint and issue data.
  • 03 Write rows to a PostgreSQL Table and have them appear as Jira Issues for programmatic ticket creation.
  • 04 Sync Confluence page metadata into a knowledge index so other tools can link to current documentation.

Common sync patterns

Issue mirror in Postgres

Jira Issues, including Custom Fields, land in PostgreSQL Tables where they can be joined with application data.

Sprint reporting views

Boards and Sprints sync into PostgreSQL so Views and Materialized Views can serve velocity and cycle-time reports.

Comment audit trail

Issue Comments are written to a PostgreSQL Table keyed by Primary and Unique Keys for a queryable history of ticket discussion.

What you can sync between Atlassian 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.

Atlassian objects PostgreSQL objects How this pairing syncs
Confluence Spaces Namespaces that scope page syncs and permissions. Sequences Generate surrogate keys for rows created by inbound syncs. Confluence Spaces is specific to Atlassian and Sequences to PostgreSQL — each maps to any object or custom field on the other side.
Jira Issues The central work item, synced two-way with CRMs, support desks, and other trackers. Custom Types and Enums Constrain synced values to a fixed set, mirroring picklist fields. Jira Issues is specific to Atlassian and Custom Types and Enums to PostgreSQL — each maps to any object or custom field on the other side.
Jira Projects Containers that scope issues, workflows, and permissions for a sync. Tables The primary sync target; rows map one-to-one to records in connected SaaS systems. Jira Projects is specific to Atlassian and Tables to PostgreSQL — each maps to any object or custom field on the other side.
Boards and Sprints Agile structures read to report on sprint contents and status. Views Read-side projections used to expose joined or filtered data to a sync. Boards and Sprints is specific to Atlassian and Views to PostgreSQL — each maps to any object or custom field on the other side.
Issue Comments Threaded discussion synced into linked tickets in external systems. Materialized Views Precomputed result sets synced outward on a refresh schedule. Issue Comments is specific to Atlassian and Materialized Views to PostgreSQL — each maps to any object or custom field on the other side.
Attachments Files on issues mirrored to paired records where needed. Schemas Namespaces that scope which tables a sync reads and writes. Attachments is specific to Atlassian and Schemas to PostgreSQL — each maps to any object or custom field on the other side.

How changes propagate between Atlassian 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.

Atlassian PostgreSQL Sub-second propagation

DetectionAtlassian notifies Stacksync of record changes through webhook events. Webhooks on issue and page events, plus JQL polling on the updated timestamp for backfill.

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

PostgreSQL Atlassian 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.

DeliveryEach detected change is written to Atlassian through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

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

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Atlassian 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 Atlassian or PostgreSQL record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Atlassian and PostgreSQL.

How the Atlassian and PostgreSQL connectors work

Atlassian

Integration surface
REST APIs per product (Jira Cloud and Confluence Cloud)
Authentication
OAuth 2.0 (3LO) for apps or API tokens with basic auth for scripts
Change detection
Webhooks on issue and page events, plus JQL polling on the updated timestamp for backfill
Capabilities
read · write · webhooks

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 Atlassian 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 Atlassian 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
    Atlassian connected
    PostgreSQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Atlassian 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.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

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 483 integrations available for Atlassian and PostgreSQL.

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