Real-time
Two-way sync
Changes in Atlassian or Databricks instantly reflect in both systems. No stale data, no manual imports.
Business productivity ⇄ Data warehouse
Atlassian to Databricks integration — real-time, two-way sync
Keep Atlassian and Databricks 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
Case study
Migrated from Mulesoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Atlassian and Databricks
Get the data locked inside Atlassian into Databricks as live tables, and send results back where Atlassian can use them, without writing a pipeline.
Whatever Atlassian is used for, it accumulates data the rest of the company wants to analyze, and that data usually sits behind an API rather than in the warehouse. Building and babysitting an extraction pipeline is the tax most teams pay for it.
Stacksync syncs Workflows and Statuses, Users and Groups, Confluence Pages, Confluence Spaces from Atlassian into tables in Databricks continuously, handling schema, rate limits, and retries. Because the sync is bi-directional, results computed in Databricks can also be written back into fields in Atlassian where the tool can use them.
Common use cases
- Create Jira issues from records in other systems, such as onboarding tasks generated from a closed-won CRM deal.
- Keep Jira and a second tracker (for example a customer's Jira instance) aligned during co-delivery projects.
- Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
Where Atlassian accepts updates: operational write-back
Segments, scores, or reference values computed in Databricks sync back onto records in Atlassian, putting analysis where the work happens.
History that outlives the tool
A continuously synced copy in Databricks preserves a queryable record even as data ages out of Atlassian or gets changed inside it.
Analytics on Atlassian's data
Records and events from Atlassian land in Databricks as queryable tables, current within seconds and ready to join with the rest of the warehouse.
What you can sync between Atlassian and Databricks
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 | Databricks objects | |
|---|---|---|
| Issue Comments Threaded discussion synced into linked tickets in external systems. | Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | |
| Attachments Files on issues mirrored to paired records where needed. | Volumes Unity Catalog file storage used for staging bulk loads. | |
| Custom Fields Instance-specific fields (customfield IDs) that carry most business-specific data in syncs. | SQL Warehouses The compute endpoint a sync connects to for query execution. | |
| Workflows and Statuses Status transitions mapped to stages in the paired system. | Change Data Feed Row-level change records on Delta tables that drive incremental reads. | |
| Users and Groups Assignees and reporters matched to identities in other tools. | Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | |
| Confluence Pages Documentation content readable and writable through the Confluence REST API. | Schemas Group tables and views; syncs typically target a dedicated schema per source system. |
What ships with Atlassian ⇄ Databricks
Connect Atlassian and Databricks for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Atlassian–Databricks connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Atlassian or Databricks 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 Databricks record.
Observability
Monitoring
Track your Atlassian ⇄ Databricks 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 Databricks.
How the Atlassian and Databricks 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
Databricks
- Integration surface
- SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
- Authentication
- Personal access tokens or OAuth machine-to-machine credentials for service principals
- Change detection
- Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
- Capabilities
- read · write · CDC
- Rate limits
- Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits
How it works
How to connect Atlassian to Databricks — 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 Databricks 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
Databricks
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Atlassian and Databricks 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 ⇄ DatabricksCustomers 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 DatabricksCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Atlassian and Databricks integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Atlassian and Databricks: authenticate both systems, choose the objects to sync (such as Atlassian's Issue Comments and Attachments), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
On the Atlassian side: Workflows and Statuses, Users and Groups, Confluence Pages, Confluence Spaces, plus custom fields where Atlassian exposes them. On the Databricks side: Volumes, SQL Warehouses, Change Data Feed, Catalogs. Stacksync auto-detects both schemas and converts types between the two systems.
-
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.
-
Common patterns for Atlassian and Databricks: Where Atlassian accepts updates: operational write-back; History that outlives the tool; Analytics on Atlassian's data. Segments, scores, or reference values computed in Databricks sync back onto records in Atlassian, putting analysis where the work happens.
-
Atlassian: REST APIs per product (Jira Cloud and Confluence Cloud). Authentication: OAuth 2.0 (3LO) for apps or API tokens with basic auth for scripts. Databricks: SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution. Authentication: Personal access tokens or OAuth machine-to-machine credentials for service principals. Stacksync manages authentication, retries, and rate limits on both sides.
-
Atlassian: Each Atlassian product has its own REST API and resource model; a sync spanning Jira and Confluence talks to separate endpoints under one Atlassian identity. Databricks: Unity Catalog imposes a three-level namespace (catalog.schema.table) that governs access across workspaces. Stacksync's field mapping accounts for these differences between Atlassian and Databricks without custom code.
SECURITY
Security teams love 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 386 integrations available for Atlassian and Databricks.
Popular · 8 of 386
-
Atlassian ⇄ Yellowbrick -
Atlassian ⇄ Snowflake -
Atlassian ⇄ BigQuery -
Atlassian ⇄ MotherDuck -
Databricks ⇄ eClinicalWorks -
Databricks ⇄ Full Enrich -
Databricks ⇄ Google Sheets -
Databricks ⇄ Klaviyo
No integrations match .
