Your Jira data in BigQuery, kept current.

Install from the Atlassian Marketplace, paste a service-account key, pick your projects. Issues, history, worklogs, sprints and comments land in your dataset within minutes and stay in sync.

Try for free See what you get

Jira Cloud

Your BigQuery

Straight from Jira to your Google Cloud project. Nothing in between to trust, host or pay for.

No servers in betweenRuns inside Atlassian Forge. Data goes from Jira straight to your Google Cloud project.

Updated every 5 minutesChanges, deletions and comments arrive as they happen. Full refresh nightly.

Models includedTime in status, sprint reports and cycle time as ready-made views. dbt package too.

Set up in three steps

About ten minutes, done once. Everything happens on one screen inside Jira.

Create a service account

In Google Cloud, give it BigQuery Job User and Data Editor on one dataset. Download the JSON key.

Paste the key in Jira

The plugin checks the connection, finds your dataset location and creates the tables for you.

Choose projects and go

All projects or a few, optionally a JQL filter. The first backfill starts right away; the schedule takes over after.

One screen inside Jira

Status, run history, destination, scope and schedule — all on one page in Jira's admin. If something needs attention, it says so in plain words.

The sync overview screen inside Jira: live status, configuration summary and recent runs.

Every table, not a selection

16 tables and 11 views, created for you and kept in sync. Append-only history with _current views on top, so you can query the latest state or travel back in time. Partitioned and clustered so queries stay cheap.

16 tables — the raw data, complete

issuesall fields, custom fields as columns

changelogevery field change, who and when

commentsfull text, internal flag, deletions

worklogstime spent per person and day

sprintsdates, goals, state

boardsscrum and kanban

projectskeys, leads, categories

usersnames and account ids

statuseswith status categories

issue_typesincl. subtasks and epics

prioritiesyour priority scale

resolutionshow issues were closed

fieldsid, name and type of every field

versionsfix and affected versions

componentsper-project components

labelsevery label in use

11 views — answers, ready to chart

Point Looker Studio, Tableau, Power BI or Metabase at your dataset and these views appear as ready-made data sources. Drag them onto a chart — no SQL, no modelling, no export.

issues_currentthe latest state of every issue, deleted ones excluded — the view your dashboards start from

time_in_statushow long each issue sat in each status — find where work waits

status_transitionsevery move between statuses — cumulative flow and bottleneck charts

issue_cycle_timescreated → started → resolved per issue, in hours — lead and cycle time trends

throughput_weeklyissues resolved per week per project — delivery pace over time

sprint_reportcommitted, completed, added, removed per sprint — velocity without the spreadsheet

epic_progressdone vs total children per epic — roadmap status at a glance

open_issue_agehow old every open issue is — spot the ones going stale

assignee_workloadopen issues and estimates per person — balance the team

worklog_hours_weeklylogged hours per person per week — timesheets and billing

comments_current & worklogs_current — latest comments and worklogs, joined to live issues

Prefer to own the models in your repo? The same logic ships as a dbt package.

Your data never leaves your hands

Nothing is exfiltrated, nothing is mirrored — and we couldn't peek even if we wanted to.

Nothing flows to us

The app writes from your Jira straight into your BigQuery — the only two hosts it can reach are Google's, hard-coded in a manifest Atlassian reviews on every release.

Developers can't see your data

FreeMetrics operates no servers. There is no place where your issues, comments or keys could land on our side — structurally, not just by policy.

Verifiable, not promised

Runs entirely on Atlassian Forge inside your site; credentials sit in Atlassian's encrypted storage; uninstalling wipes everything. Check the Privacy & Security tab — it's all declared there.

Built for the person who has to keep it running

Most connectors work on day one. This one is designed to still be right in month twelve.

Deletions are captured

Deleted issues, comments and worklogs are marked in BigQuery, so your totals match Jira.

New fields appear on their own

Add a custom field in Jira and it becomes a column on the next run. Nothing to reconfigure.

Several syncs per site

Different projects, datasets and schedules. Up to ten syncs, each with its own key if you like.

Large sites welcome

Backfills resume where they stopped and respect Jira rate limits. Hundreds of thousands of issues are fine.

Clear run history

Every run, what it wrote and how long it took. If something fails you see why, in plain words.

Your key stays yours

Stored encrypted in Atlassian storage, never shown again, removable in one click.

How it compares

Against the most used BigQuery connector on the Marketplace.

	BigQuery Sync for Jira	Other connectors
Where it runs	Inside Atlassian (Forge), nothing else	Vendor servers between Jira and your cloud
Refresh	Every 5 minutes, plus nightly reconcile	Hourly or manual
Tables	All 16, always	Picked one by one per data source
History	Append-only with time travel	Overwrites
Models	Views and dbt package included	Extra
Support	Chat with the engineers, replies within hours (CET)	Ticket, 1–2 business days

Try it on your own site

Free for 30 days, and free forever for up to 10 users. Cancel from Jira at any time.

Start free trial Read the setup guide