Real-time
Two-way sync
Changes in Jira or MotherDuck instantly reflect in both systems. No stale data, no manual imports.
Developer tools ⇄ Data warehouse
Jira to MotherDuck integration — real-time, two-way sync
Keep Jira and MotherDuck 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 Jira and MotherDuck
Close the gap between analytics and operations: MotherDuck holds the record while Jira runs the day-to-day work, and Stacksync keeps the two in step in real time, in both directions.
MotherDuck is the central store where teams keep Databases, Schemas, Tables, Views for reporting and analysis; Jira runs the operational side of engineering work — tracking issues, moving messages and events, watching systems, and managing users and access. The two overlap wherever the same operational data matters to both: the Projects, Comments, Worklogs, Sprints produced in Jira are exactly what analysts want to measure in MotherDuck, and the curated rows in MotherDuck are what should drive the next action in Jira. When that overlap is bridged by nightly ETL or hand-written scripts, dashboards lag a day behind reality and the tools that should react to warehouse signals never see them.
Stacksync syncs Databases, Schemas, Tables, Views in MotherDuck with Projects, Comments, Worklogs, Sprints in Jira field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync matches records on a stable external key, keeps every copy consistent, and resolves conflicts by rules you set — so analytics and operations work from the same current data instead of two drifting copies.
Common use cases
- 01 Land CRM and operational database records in MotherDuck so a small team gets warehouse-style analytics without cluster management
- 02 Sync modeled MotherDuck tables outward to operational tools for activation
- 03 Consolidate multiple Jira Projects or sites into one database, mapping Projects, Components, and Versions for cross-team reporting.
- 04 Two-way sync Issues and their status, assignee, and story points with a Postgres database so teams query and update sprint data in SQL.
Common sync patterns
One shared record, kept consistent
Where both systems track the same entity, a change on either side propagates to the other, ending the manual reconciliation between the operational copy and the warehouse copy.
Keep user and access records aligned
Where Jira manages users, directory, or access data, those records stay current in MotherDuck — and can be provisioned back from it — so ownership and permissions match across both.
Operational data lands in MotherDuck for analytics
Records created in Jira — issues, events, messages, metrics, or user changes — replicate into MotherDuck tables as they happen, so reporting runs on current data instead of last night's export.
What you can sync between Jira and MotherDuck
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.
| Jira objects | MotherDuck objects | How this pairing syncs | |
|---|---|---|---|
| Comments Discussion threads on Issues; in v3 the body is Atlassian Document Format JSON, so rich text is preserved when syncing to and from other systems. | Schemas Namespaces within a database used to organize synced tables. | Comments is specific to Jira and Schemas to MotherDuck — each maps to any object or custom field on the other side. | |
| Worklogs Time-tracking entries against Issues; read into warehouses for effort and capacity reporting, or written back from timesheet tools. | Tables The main landing target for synced records and source for analysis. | Worklogs is specific to Jira and Tables to MotherDuck — each maps to any object or custom field on the other side. | |
| Sprints Agile iterations from the Jira Software API; synced to report scope, velocity, and burndown, and to move Issues between sprints. | Views Modeled projections used as outbound sync sources. | Sprints is specific to Jira and Views to MotherDuck — each maps to any object or custom field on the other side. | |
| Versions Release / fix-version records per Project; synced to align roadmap and release tools on what ships in each version. | Database Shares Read-only copies of a database shared with other users or teams. | Versions is specific to Jira and Database Shares to MotherDuck — each maps to any object or custom field on the other side. | |
| Components Sub-project categories used to route and group Issues; synced so ownership and triage stay consistent across tools. | Attached Local DuckDB Databases Local files attached alongside cloud databases for hybrid queries. | Components is specific to Jira and Attached Local DuckDB Databases to MotherDuck — each maps to any object or custom field on the other side. | |
| Users Account records referenced as reporters, assignees, and watchers; read to resolve accountId to a person when mapping Issue ownership. | Databases Cloud-hosted DuckDB databases that scope a sync's reads and writes. | Users is specific to Jira and Databases to MotherDuck — each maps to any object or custom field on the other side. |
How changes propagate between Jira and MotherDuck
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.
Jira MotherDuck Sub-second propagation
DetectionJira notifies Stacksync of record changes through webhook events. Jira webhooks (jira:issue_created / _updated / _deleted plus comment and worklog events) for near-real-time.
DeliveryEach detected change is applied to MotherDuck as a row-level write, with types converted between the two schemas.
MotherDuck Jira Interval-based propagation
DetectionStacksync polls MotherDuck for changes on an incremental schedule, reading only records changed since the previous pass. Polling.
DeliveryEach detected change is written to Jira through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- Jira: Cost-based (points) model; 429 responses return Retry-After and X-RateLimit-* headers. JQL search costs far more than single-issue reads.
- MotherDuck: Subject to the platform's compute and concurrency limits rather than per-request API rate limits.
What ships with Jira ⇄ MotherDuck
Connect Jira and MotherDuck for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Jira–MotherDuck connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Jira or MotherDuck 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 Jira or MotherDuck record.
Observability
Monitoring
Track your Jira ⇄ MotherDuck sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Jira and MotherDuck.
How the Jira and MotherDuck connectors work
Jira
- Integration surface
- REST API v2 and v3 plus the Jira Software (Agile) REST API
- Authentication
- OAuth 2.0 (3LO) for apps, or Basic auth with an Atlassian account email plus API token
- Change detection
- Jira webhooks (jira:issue_created / _updated / _deleted plus comment and worklog events) for near-real-time; incremental JQL polling on the issue updated timestamp as a best-effort reconciliation fallback.
- Capabilities
- read · write · webhooks
- Rate limits
- Cost-based (points) model; 429 responses return Retry-After and X-RateLimit-* headers. JQL search costs far more than single-issue reads.
MotherDuck
- Integration surface
- SQL through DuckDB clients and drivers using a MotherDuck (md:) connection
- Authentication
- Access token created in MotherDuck (Settings > General > Create Token), pasted into Stacksync; database name and schema configurable if not using defaults
- Change detection
- Polling; no log-based CDC or webhook surface is exposed
- Capabilities
- read · write
- Rate limits
- Subject to the platform's compute and concurrency limits rather than per-request API rate limits
How it works
How to connect Jira to MotherDuck — 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 Jira and MotherDuck with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Jira
connected
MotherDuck
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Jira and MotherDuck 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 · Jira ⇄ MotherDuckCustomers 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.
Jira MotherDuckCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Jira and MotherDuck integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Jira and MotherDuck: authenticate both systems, choose the objects to sync (such as Jira's Comments and Worklogs), map fields visually, and changes propagate both ways in milliseconds — no code required.
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MotherDuck: MotherDuck is built on DuckDB, so integrations use DuckDB SQL and connect through standard DuckDB client libraries with an md: connection string. Jira: Custom fields are keyed by internal IDs like customfield_10011, discoverable through the Get fields endpoint. Stacksync's field mapping accounts for these differences between Jira and MotherDuck without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Jira and MotherDuck records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Jira and MotherDuck connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Jira–MotherDuck integration in-house.
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Yes — Stacksync ships production-grade connectors for both Jira and MotherDuck. 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 Jira: Jira webhooks (jira:issue_created / _updated / _deleted plus comment and worklog events) for near-real-time; incremental JQL polling on the issue updated timestamp as a best-effort reconciliation fallback. On MotherDuck: Polling; no log-based CDC or webhook surface is exposed. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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
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
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Popular · 6 of 328
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Jira ⇄ Apache Pinot -
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MotherDuck ⇄ Jms -
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No integrations match .
