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Atlassian to AWS Aurora MySQL integration — real-time, two-way sync

Keep Atlassian and AWS Aurora MySQL 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 AWS Aurora MySQL

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

Engineering and data teams sync Atlassian to AWS Aurora MySQL to work with Jira data as ordinary Tables and Rows instead of API calls. Jira Issues, Issue Comments, and Custom Fields land in Aurora MySQL schemas where they can be joined, queried, and written back with SQL. This is an operational mirror: the database becomes the programmable interface to Jira.

Stacksync mirrors Jira Projects, Boards and Sprints, Issue Comments, Attachments from Atlassian into Foreign keys, Stored procedures and triggers, Databases (schemas), Tables in AWS Aurora MySQL 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 Build internal dashboards on Aurora MySQL Views over Jira Issues without hitting Jira API rate limits.
  • 02 Automate bulk Issue updates from application code writing plain SQL Rows.
  • 03 Join Jira Projects data with other tables in the same database for cross-system reporting.
  • 04 Sync Confluence page metadata into a knowledge index so other tools can link to current documentation.

Common sync patterns

Jira as SQL

Jira Issues and Custom Fields sync into Aurora MySQL Tables and Rows for querying, joins, and internal tooling.

Bi-directional issue updates

updating a Row in Aurora MySQL writes back to the corresponding Jira Issue, including status and Custom Fields.

Sprint reporting tables

Boards and Sprints data lands in dedicated Tables with Primary keys and indexes for fast reporting queries.

What you can sync between Atlassian and AWS Aurora MySQL

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 AWS Aurora MySQL objects How this pairing syncs
Confluence Spaces Namespaces that scope page syncs and permissions. Databases (schemas) Logical namespaces that scope which tables a sync connection can see. Confluence Spaces is specific to Atlassian and Databases (schemas) to AWS Aurora MySQL — 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 The primary sync unit; each table maps one-to-one to a table or object in the paired system. Jira Issues is specific to Atlassian and Tables to AWS Aurora MySQL — each maps to any object or custom field on the other side.
Jira Projects Containers that scope issues, workflows, and permissions for a sync. Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. Jira Projects is specific to Atlassian and Rows to AWS Aurora MySQL — 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. Columns MySQL data types are mapped to the paired system's field types during schema setup. Boards and Sprints is specific to Atlassian and Columns to AWS Aurora MySQL — each maps to any object or custom field on the other side.
Issue Comments Threaded discussion synced into linked tickets in external systems. Primary keys and indexes Used to match rows across systems and keep incremental syncs efficient. Issue Comments is specific to Atlassian and Primary keys and indexes to AWS Aurora MySQL — each maps to any object or custom field on the other side.
Attachments Files on issues mirrored to paired records where needed. Views Can serve as read-only sync sources for derived or filtered datasets. Attachments is specific to Atlassian and Views to AWS Aurora MySQL — each maps to any object or custom field on the other side.

How changes propagate between Atlassian and AWS Aurora MySQL

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 AWS Aurora MySQL 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 AWS Aurora MySQL as a row-level write, with types converted between the two schemas.

AWS Aurora MySQL Atlassian Sub-second propagation

DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback.

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

What ships with Atlassian ⇄ AWS Aurora MySQL

Connect Atlassian and AWS Aurora MySQL for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Atlassian or AWS Aurora MySQL 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 AWS Aurora MySQL record.

Observability

Monitoring

Track your Atlassian ⇄ AWS Aurora MySQL 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 AWS Aurora MySQL.

How the Atlassian and AWS Aurora MySQL 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

AWS Aurora MySQL

Integration surface
SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback
Capabilities
read · write · CDC
How it works

How to connect Atlassian to AWS Aurora MySQL — 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 AWS Aurora MySQL 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
    AWS Aurora MySQL connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Atlassian and AWS Aurora MySQL 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 388 integrations available for Atlassian and AWS Aurora MySQL.

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