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Data warehouse ⇄ Developer tools

Dremio to Jira integration — real-time, two-way sync

Keep Dremio and Jira 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 Dremio and Jira

Close the gap between analytics and operations: Dremio holds the record while Jira runs the day-to-day work, and Stacksync keeps the two in step in real time, in both directions.

Dremio is the central store where teams keep Physical datasets, Virtual datasets (views), Apache Iceberg tables, Spaces and folders 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 Users, Issues, Projects, Comments produced in Jira are exactly what analysts want to measure in Dremio, and the curated rows in Dremio 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 Physical datasets, Virtual datasets (views), Apache Iceberg tables, Spaces and folders in Dremio with Users, Issues, Projects, Comments 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 Sync curated Dremio views into an operational Postgres so applications get low-latency access to lakehouse data.
  • 02 Reverse-ETL aggregates computed over lake data out to CRMs and finance tools for business users.
  • 03 Sync Sprints and Boards with a capacity-planning tool so estimates and iteration scope stay aligned.
  • 04 Consolidate multiple Jira Projects or sites into one database, mapping Projects, Components, and Versions for cross-team reporting.

Common sync patterns

No batch jobs to babysit

New and changed records move field by field the moment they change, replacing scheduled ETL and one-off scripts that fail quietly and leave stale rows behind.

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 Dremio — and can be provisioned back from it — so ownership and permissions match across both.

What you can sync between Dremio and Jira

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.

Dremio objects Jira objects How this pairing syncs
Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. Versions Release / fix-version records per Project; synced to align roadmap and release tools on what ships in each version. Apache Iceberg tables is specific to Dremio and Versions to Jira — each maps to any object or custom field on the other side.
Spaces and folders Namespaces that organize virtual datasets and govern access. Components Sub-project categories used to route and group Issues; synced so ownership and triage stay consistent across tools. Spaces and folders is specific to Dremio and Components to Jira — each maps to any object or custom field on the other side.
Reflections Materialized accelerations that make repeated extraction queries cheaper. Users Account records referenced as reporters, assignees, and watchers; read to resolve accountId to a person when mapping Issue ownership. Reflections is specific to Dremio and Users to Jira — each maps to any object or custom field on the other side.
Jobs Query execution records useful for monitoring sync workloads. Issues Core work items (stories, bugs, tasks, epics, sub-tasks); synced two-way with databases and other trackers, keyed by issue key with an updated field for incrementals. Jobs is specific to Dremio and Issues to Jira — each maps to any object or custom field on the other side.
Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. Projects Containers that group Issues, workflows, and permissions; usually read to segment syncs by team, or written when standing up a new project. Sources is specific to Dremio and Projects to Jira — each maps to any object or custom field on the other side.
Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. 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. Physical datasets is specific to Dremio and Comments to Jira — each maps to any object or custom field on the other side.

How changes propagate between Dremio and Jira

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.

Dremio Jira Interval-based propagation

DetectionStacksync polls Dremio for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL.

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

Jira Dremio 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 Dremio as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Dremio: Bounded by engine capacity and workload management rather than API rate limits.
  • Jira: Cost-based (points) model; 429 responses return Retry-After and X-RateLimit-* headers. JQL search costs far more than single-issue reads.
What ships with Dremio ⇄ Jira

Connect Dremio and Jira for flexible, real-time data sync.

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

Real-time

Two-way sync

Changes in Dremio or Jira instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Dremio or Jira 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 Dremio or Jira record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Dremio and Jira.

How the Dremio and Jira connectors work

Dremio

Integration surface
Arrow Flight SQL, JDBC/ODBC, and a REST API
Authentication
Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
Change detection
Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
Capabilities
read · write
Rate limits
Bounded by engine capacity and workload management rather than API rate limits

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.
How it works

How to connect Dremio to Jira — 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 Dremio and Jira 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
    Dremio connected
    Jira connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Dremio and Jira 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 · Dremio ⇄ Jira
    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
    Dremio Jira
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Dremio and Jira 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 313 integrations available for Dremio and Jira.

Popular · 6 of 313
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