TL;DR Short answer: Jira's built-in export to CSV or Excel is fine for a list of issues, but it caps at 1,000 issues per export in Jira Cloud and contains only current values, not the changelog of status moves. Cycle time, throughput and time in review all come from that changelog, so delivery metrics need a tool that reads Jira's API on a schedule. Eight that do:
Airbyte Fivetran Stitch Data Hevo Data Boomi Data Integration Matillion Meltano Apache Airflow One Jira constraint: Atlassian removed the old Jira Cloud search endpoints in 2025, and the replacement JQL search uses token pagination, rejects unbounded queries and is eventually consistent. Any tool still on the old API stopped working, so confirm a connector is current before you rely on it, and expect very recent edits to land on the next sync.Why is it hard to export data from Jira? Jira is Atlassian's issue tracker, and the data inside it is what delivery metrics are built from: cycle time, throughput, sprint carry-over and how long work sits in review. Getting that out is harder than it looks. Issue records show only where each issue is now; the history of when it moved between statuses lives in a separate changelog, one set of entries per issue. Custom fields are stored as field IDs that need mapping to names, and a large instance has hundreds of them.
If you still run Jira Data Center, note that Atlassian ends it on 28 March 2029, when instances become read-only, and stopped selling new Data Center subscriptions in March 2026. Pipelines built today should expect to point at Jira Cloud.
How do the Jira ETL tools compare? Tool Built for Destinations Hosting Pricing basis Airbyte Data teams, many sources Any Cloud or self-hosted Free self-hosted; Cloud volume-based; Pro and Flex capacity-based Fivetran Data teams wanting no upkeep Any Cloud Monthly active rows Stitch Data Teams with simple needs Major warehouses Cloud Row volume tiers Hevo Data Teams without engineers Major warehouses Cloud Events Boomi Data Integration Analytics teams wanting orchestration Major warehouses Cloud Credits Matillion In-warehouse transformation Snowflake, Databricks, Redshift Your cloud account Credits Meltano Engineers who want code Any Singer target Self-hosted Free, open source Apache Airflow Orchestrating custom jobs Whatever you write Self-hosted or managed Free, you run it
Which Jira ETL tools should you consider? Every option below reads Jira natively. When you shortlist, check that it extracts the issue changelog, not just issues, and that it is current with Atlassian's new JQL search API.
1. Airbyte Airbyte is an open-source data integration platform with 700+ connectors and a community of 25,000+ data engineers. Its Jira source reads issues together with their changelog, plus sprints, sprint issues, worklogs, boards, projects and users, with incremental sync so each run fetches only what changed. You can scope it to specific projects, and it loads into any Airbyte destination for modelling with dbt.
What's unique about Airbyte? Jira data is most useful next to GitHub, incident and on-call data, and Airbyte moves all of them through one pipeline, so lead time from commit to deploy is a join rather than a project. Connectors are open source and editable, and the Connector Development Kit covers anything else. It runs as self-hosted open source, on Airbyte Cloud, or as Enterprise Flex.
2. Fivetran Fivetran is a closed-source, managed ELT service with 700+ connectors. Its Jira connector includes changelog and sprint tables and handles schema changes for you. It bills on monthly active rows, and busy Jira projects update issues constantly, which counts toward the bill.
Here is a closer look at Airbyte vs Fivetran .
3. Stitch Data Stitch is a cloud extract-and-load platform with 140+ connectors, including Jira, originally built on the Singer specification. It has no user-defined transformations and suits straightforward loading. Qlik acquired Talend, and Stitch with it, in 2023, and now publishes a migration path from Stitch to Qlik Talend Cloud, which is worth weighing before committing.
Here is a closer look at Airbyte vs Stitch .
4. Hevo Data Hevo Data is a no-code cloud ELT platform with 150+ connectors, including Jira, and built-in transformation applied before data reaches the destination. It is cloud-only and billed on events, which suits engineering-operations teams without a dedicated data engineer.
5. Boomi Data Integration (formerly Rivery) Boomi acquired Rivery in December 2024 and now sells it as Boomi Data Integration. It has a Jira connector and bundles transformation and orchestration in one cloud platform. Pricing is in credits that vary by connector, which can make cost hard to model.
6. Matillion Matillion's cloud platform has a Jira connector and pushes transformation down into Snowflake, Databricks or Redshift, with dbt support since v1.70, so reshaping issue and changelog tables uses compute you already pay for. It bills in credits tied to job runtime and makes most sense when Matillion already runs your warehouse transformations.
Here is a closer look at Airbyte vs Matillion .
7. Meltano Meltano is an open-source, CLI-first platform spun out of GitLab that manages taps and targets as a version-controlled project, with Jira extractors on Meltano Hub. It suits teams who want pipelines defined in code. Check that the extractor you choose has moved to Atlassian's new JQL search endpoint, since older community taps that relied on the removed endpoints no longer work.
8. Apache Airflow Apache Airflow is an open-source orchestrator, not an ETL tool. Its Atlassian Jira provider gives you a hook and operators for calling the Jira API, but you write the pagination, changelog extraction, custom-field mapping and loading yourself. Its real role is scheduling a connector-based tool such as Airbyte, or dbt models on Jira data already in the warehouse.
Here is a closer look at Airbyte vs Airflow .
How do you export Jira issues to CSV or Excel? In Jira Cloud, run a search or open a filter in the issue list, choose Export, and pick CSV with all fields or with only the current fields shown. The CSV opens directly in Excel or Google Sheets. It works well for a one-off list, with three limits worth knowing:
1,000 issues per export: larger result sets have to be split into several exports by date range or project, then stitched back together.Current values only: the export shows each issue's status today, not the history of how it got there, so it cannot give you cycle time or time in status.Manual and point-in-time: every refresh is another download, and comparing exports over weeks quickly becomes error-prone.For anything ongoing, or anything about flow rather than counts, use one of the tools above to keep issues and their changelog in a warehouse, then query or export from there.
How do you measure cycle time from Jira data? Cycle time is the elapsed time between an issue entering an in-progress status and reaching done. Jira records each status transition in the changelog with a timestamp, so once the changelog is in Snowflake, BigQuery or another warehouse, cycle time is a query: take each issue's first transition into your in-progress statuses and its last transition into done, and subtract.
Two details make the numbers trustworthy. Map statuses to stages explicitly, since every team names its workflow differently. And decide how to treat issues that reopen, either by measuring to the final done or by summing each in-progress interval. With the changelog alongside your GitHub and deployment data, the same model extends to lead time for changes and other DORA metrics.
How do you start exporting Jira data with Airbyte? Step 1: Set up Jira as a source Create an API token from your Atlassian account's security settings, using an account that can browse the projects you need. In Airbyte, open Sources, choose New source and select Jira, then enter your Jira domain, the account email and the API token, optionally list the projects to sync, and set a start date. Run the connection test.
Step 2: Set up a destination Choose where the data lands: a cloud warehouse such as Snowflake, BigQuery or Redshift, a database such as Postgres, a data lake or cloud storage.
Step 3: Configure the connection Select issues and their changelog, plus sprints and worklogs if you track them, use incremental sync, and set a schedule. Hourly or daily is usually enough for delivery metrics.
The process is the same in Airbyte Open Source, which you can deploy yourself , and in Airbyte Cloud, which you can try free for 14 days .
Which Jira ETL tool should you choose? If you only need a list of issues now and then, Jira's own CSV export is enough. For delivery metrics, choose a tool that extracts the changelog and is current with Atlassian's JQL search API. Airbyte brings Jira into the same warehouse as your code and deployment data, self-hosted or in Cloud, and Fivetran does it fully managed if the row-based bill works for busy projects. Hevo, Stitch and Boomi suit simpler no-code needs, Matillion and Meltano fit where they already run, and Airflow orchestrates rather than extracts.
For tools that cover more than Jira, see our guide to the top data integration tools .
Sync Jira with Airbyte Move Jira issues, changelogs, sprints and worklogs into Snowflake, BigQuery or any Airbyte destination, next to your GitHub and deployment data, self-hosted or in Airbyte Cloud.
Try Airbyte Cloud free for 14 days or talk to our team .
Frequently Asked Questions (FAQs) 1. How do you export more than 1,000 issues from Jira? Jira Cloud's CSV export is capped at 1,000 issues, so either split the search into smaller exports by date range or project, or use an ETL tool that reads the Jira API, which has no such cap and keeps the data current.
2. Does the Jira CSV export include issue history? No. It contains current field values only. The history of status changes lives in the issue changelog, which you need to extract through the API to measure cycle time or time in status.
3. Why did my Jira integration stop working? If it relied on the old /rest/api/2 or /rest/api/3 search endpoints, Atlassian removed them during 2025 and they now return an error. Tools and scripts need to use the new JQL search endpoint, which pages with a token rather than a start position.
4. How do you get Jira data into Snowflake? Use an ETL tool with a Jira source and a Snowflake destination, such as Airbyte, Fivetran or Hevo, and include the changelog alongside issues so you can model delivery metrics in Snowflake.
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