Real-time
Two-way sync
Changes in Atlassian or Render Postgres instantly reflect in both systems. No stale data, no manual imports.
Business productivity ⇄ Database
Atlassian to Render Postgres integration — real-time, two-way sync
Keep Atlassian and Render Postgres in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
- 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 Render Postgres
Mirror Atlassian's data into Render Postgres so your own code can read and write it like any other table, with changes flowing both ways in seconds.
Engineers integrate with tools like Atlassian through APIs, which means auth, pagination, rate limits, webhooks, and retry logic, all maintained forever and all different for every tool. Meanwhile the data would be trivial to use if it simply lived in Render Postgres.
Stacksync mirrors Boards and Sprints, Issue Comments, Attachments, Custom Fields from Atlassian into Materialized Views, Schemas, Columns and Types, Indexes and Constraints in Render Postgres 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 Sync Confluence page metadata into a knowledge index so other tools can link to current documentation.
- 02 Mirror Jira projects into a SQL database for engineering throughput and SLA reporting.
- 03 Capture row-level changes via logical replication and propagate them to a warehouse or another database in near real time.
- 04 Serve as the operational read/write store behind internal tools while Stacksync keeps it consistent with SaaS systems of record.
Common sync patterns
One integration pattern for the whole stack
Every synced tool looks the same from the database, so each new integration is configuration, not a new codebase.
Read Atlassian with a query
Records from Atlassian are ordinary rows in Render Postgres; join them, index them, and use them in application logic without touching the vendor API.
Automate Atlassian from your codebase
Write to the synced tables in Render Postgres and Stacksync propagates the change into Atlassian, replacing custom integration code.
What you can sync between Atlassian and Render Postgres
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 | Render Postgres objects | How this pairing syncs | |
|---|---|---|---|
| Custom Fields Instance-specific fields (customfield IDs) that carry most business-specific data in syncs. | Views Saved queries exposed as read-only relations; read out to BI tools or downstream syncs without duplicating transformation logic. | Custom Fields is specific to Atlassian and Views to Render Postgres — each maps to any object or custom field on the other side. | |
| Workflows and Statuses Status transitions mapped to stages in the paired system. | Materialized Views Precomputed query results refreshed on demand; read for fast reporting tables that downstream systems can consume. | Workflows and Statuses is specific to Atlassian and Materialized Views to Render Postgres — each maps to any object or custom field on the other side. | |
| Users and Groups Assignees and reporters matched to identities in other tools. | Schemas Namespaces that organize tables per app or environment; sync targets are scoped per schema to keep synced data isolated and tidy. | Users and Groups is specific to Atlassian and Schemas to Render Postgres — each maps to any object or custom field on the other side. | |
| Confluence Pages Documentation content readable and writable through the Confluence REST API. | Columns and Types Full Postgres type system including JSONB and arrays; field mappings preserve native types instead of flattening to strings. | Confluence Pages is specific to Atlassian and Columns and Types to Render Postgres — each maps to any object or custom field on the other side. | |
| Confluence Spaces Namespaces that scope page syncs and permissions. | Indexes and Constraints Primary keys, unique constraints, and foreign keys; unique keys drive idempotent upserts and conflict resolution during sync. | Confluence Spaces is specific to Atlassian and Indexes and Constraints to Render Postgres — 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. | Tables Relational tables with full column typing; synced two-way with CRMs, ERPs, and SaaS apps so application data is queryable as plain Postgres rows. | Jira Issues is specific to Atlassian and Tables to Render Postgres — each maps to any object or custom field on the other side. |
How changes propagate between Atlassian and Render Postgres
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 Render Postgres 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 Render Postgres as a row-level write, with types converted between the two schemas.
Render Postgres Atlassian Sub-second propagation
DetectionChanges in Render Postgres are captured at the source via change data capture — no polling loop against its API. Logical replication via WAL and replication slots for change data capture when enabled on the instance, with timestamp or cursor-based polling as the.
DeliveryEach detected change is written to Atlassian through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- Render Postgres: No API rate limits — throughput is bounded by the instance's plan (CPU, RAM, connection limit); connection pooling is recommended since managed plans cap concurrent connections.
What ships with Atlassian ⇄ Render Postgres
Connect Atlassian and Render Postgres for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Atlassian–Render Postgres connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Atlassian or Render Postgres 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 Render Postgres record.
Observability
Monitoring
Track your Atlassian ⇄ Render Postgres 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 Render Postgres.
How the Atlassian and Render Postgres 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
Render Postgres
- Integration surface
- PostgreSQL wire protocol (managed Postgres on Render)
- Authentication
- Standard Postgres connection string — host, port, database, user, password with TLS; Render provides internal and external connection URLs and IP allowlisting
- Change detection
- Logical replication via WAL and replication slots for change data capture when enabled on the instance, with timestamp or cursor-based polling as the fallback
- Capabilities
- read · write · CDC
- Rate limits
- No API rate limits — throughput is bounded by the instance's plan (CPU, RAM, connection limit); connection pooling is recommended since managed plans cap concurrent connections.
How it works
How to connect Atlassian to Render Postgres — 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.
- 01
Connect your apps
Authenticate Atlassian and Render Postgres with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Atlassian
connected
Render Postgres
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Atlassian and Render Postgres 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.
objects · Atlassian ⇄ Render PostgresCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Atlassian Render PostgresCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Atlassian and Render Postgres integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Atlassian and Render Postgres: authenticate both systems, choose the objects to sync (such as Atlassian's Custom Fields and Workflows and Statuses), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Atlassian and Render Postgres. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Atlassian: Webhooks on issue and page events, plus JQL polling on the updated timestamp for backfill. On Render Postgres: Logical replication via WAL and replication slots for change data capture when enabled on the instance, with timestamp or cursor-based polling as the fallback. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Atlassian side: Boards and Sprints, Issue Comments, Attachments, Custom Fields, plus custom fields where Atlassian exposes them. On the Render Postgres side: Materialized Views, Schemas, Columns and Types, Indexes and Constraints. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Atlassian and Render Postgres: One integration pattern for the whole stack; Read Atlassian with a query; Automate Atlassian from your codebase. Every synced tool looks the same from the database, so each new integration is configuration, not a new codebase.
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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 354 integrations available for Atlassian and Render Postgres.
Popular · 8 of 354
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Atlassian ⇄ Google AlloyDB -
Atlassian ⇄ Dynamo DB -
Atlassian ⇄ IBM Db2 -
Atlassian ⇄ Firebase -
Render Postgres ⇄ Twilio -
Render Postgres ⇄ GitHub -
Render Postgres ⇄ Braze -
Render Postgres ⇄ ConnectWise
No integrations match .
