TL;DR Short answer: to export data from Jira at any useful scale you need a tool that works against its REST API, because the built-in CSV export caps out and loses the issue history that delivery metrics depend on. Thirteen options:
Airbyte Fivetran Stitch Matillion Airflow Talend Pentaho Informatica PowerCenter Microsoft SSIS Singer Rivery Hevo Data Meltano The other nine are Airflow, Talend, Pentaho, Informatica PowerCenter, Microsoft SSIS, Singer, Rivery, Hevo Data and Meltano, covered in full below.
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, how long work sits in review. Getting that out is harder than it looks. The built-in CSV export is capped and flattens everything to current state, so the changelog, which records when each issue moved between statuses, does not come with it. Without the changelog you can count issues but you cannot measure flow. That is why teams reach for an ETL tool: it reads the REST API, paginates through issues, and pulls changelog, sprint and worklog data alongside the issue records.
For simplicity, this guide uses "Jira ETL" to refer to all data integration tools, ETL and ELT alike, that can read from Jira.
Why export Jira data to a warehouse? Companies might do Jira ETL for several reasons:
Business intelligence: Jira data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operationsCompliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.Overall, ETL from Jira allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.
How should you choose a Jira ETL tool? As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your Jira ETL solution.
Which Jira ETL tools should you consider?
Tool Type Jira coverage Pricing
Airbyte Open-source ELT Pre-built connector with issues, changelog, sprints, worklogs and boards; incremental sync Free self-hosted; Cloud capacity-based Fivetran Managed ELT Pre-built connector including changelog and sprint tables Monthly active rows Stitch Extract and load Pre-built connector; no user-defined transformations Rows per month Matillion ELT Loads Jira data, then reshapes it in the warehouse Credits Apache Airflow Orchestrator You write the API calls and pagination yourself Free, you run it Talend Integration platform REST components in the studio Quoted, no free tier Pentaho ETL and analytics REST client step Open core, paid enterprise Informatica PowerCenter Enterprise ETL REST connector, heavier setup Quoted; 10.5 out of standard support Microsoft SSIS ETL Custom script component in C# Included with SQL Server Singer Tap and target spec tap-jira exists but is community-maintained Free Rivery Cloud ELT Pre-built connector; now Boomi Data Integration Credits Hevo Data Managed ELT Pre-built connector, vendor-maintained only Events per month Meltano CLI-first ELT Singer taps managed as a Git project Free, you run it
Here are the top Jira ETL tools based on their popularity and the criteria listed above:
1. Airbyte Airbyte is the leading open-source ELT platform, created in July 2020. It offers 700+ connectors and a community of more than 25,000 members. Its Jira connector pulls issues, changelog, sprints, worklogs and boards, with incremental sync so you are not refetching the whole project each run.
What's unique about Airbyte? Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
2. Fivetran Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has 750+ sources and over 5,000 customers.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran? Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Data Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch is a cloud extract-and-load platform with 140+ connectors, originally built on the open-source Singer specification. It has no user-defined transformations and no log-based change capture.
What's unique about Stitch? Since Qlik acquired Talend, and Stitch with it, in 2023, Stitch has become one product line inside a much larger portfolio, and Qlik now publishes a formal migration path from Stitch to Qlik Talend Cloud. It is still quick to set up, but that direction of travel is worth weighing first.
Here are more insights on the differentiations between Airbyte and Stitch .
What else should you consider? 4. Matillion Matillion is an ELT platform created in 2011, built around pushdown transformation that runs inside your cloud warehouse. It supports 100+ connectors and covers extract, load and transform. It also integrates with dbt, which has shipped with the product since version 1.70.
What's unique about Matillion? Running in your own cloud account means Jira data stays inside your infrastructure, though a multi-cloud setup may need more than one instance. Transformation is pushed down to the warehouse, so reshaping issue and changelog tables uses compute you already pay for.
Here are more insights on the differentiations between Airbyte and Matillion .
5. Airflow Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow? Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow .
6. Talend Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend? Talend pairs integration with data quality and governance. Two things to check before shortlisting it: Qlik acquired Talend in 2023 and now sells it as Qlik Talend Cloud, and Talend Open Studio, the free open-source edition, was retired on 31 January 2024, so there is no free tier or self-serve route in.
7. Pentaho Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho? What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
8. Informatica PowerCenter Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
9. Microsoft SQL Server Integration Services (SSIS) MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.
10. Singer Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
11. Rivery Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
12. HevoData HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
13. Meltano Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
All those ETL tools are not specific to Jira, you might also find some other specific data loader for Jira data. But you will most likely not want to be loading data from only Jira in your data stores.
Jira's API provides access to a wide range of data related to project management and issue tracking. The following are the categories of data that can be accessed through Jira's API:
1. Issues: This includes all the information related to the issues such as issue type, status, priority, description, comments, attachments, and more.
2. Projects: This includes information about the projects such as project name, description, project lead, and more.
3. Users: This includes information about the users such as user name, email address, and more.
4. Workflows: This includes information about the workflows such as workflow name, workflow steps, and more.
5. Custom fields: This includes information about the custom fields such as custom field name, type, and more.
6. Dashboards: This includes information about the dashboards such as dashboard name, description, and more.
7. Reports: This includes information about the reports such as report name, description, and more.
8. Agile boards: This includes information about the agile boards such as board name, board type, and more.
Overall, Jira's API provides access to a vast amount of data that can be used to improve project management and issue tracking.
How do you start exporting data from Jira? If you decide to test Airbyte, you can start analyzing your Jira data within minutes in three easy steps:
Step 1: Set up Jira as a source connector 1. First, navigate to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "Add Source" button in the top right corner of the screen.
3. Select "Jira" from the list of available sources.
4. Enter a name for your Jira source connector and click "Next".
5. Enter your Jira credentials, including the Jira URL, email address, and API token.
6. Test the connection to ensure that the credentials are correct and the connection is successful.
7. Select the Jira projects and issue types that you want to replicate in Airbyte.
8. Choose the replication frequency and any other settings that you want to apply to your Jira source connector.
9. Click "Create Source" to save your Jira source connector and begin replicating data from Jira to Airbyte.
It is important to note that the specific steps for connecting your Jira source connector may vary depending on your specific use case and the version of Jira that you are using. For more detailed instructions and troubleshooting tips, refer to the Airbyte documentation or consult with a Jira expert.
Step 2: Set up a destination for your extracted Jira data Choose the destination where you want your Jira data to land. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.
Step 3: Configure the Jira data pipeline in Airbyte Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes , or Airbyte Cloud which you can try here , free for 14 days.
Which Jira ETL tool should you choose? This article outlined the criteria that you should consider when choosing a data integration solution for Jira ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing Jira ETL and how to best do it.
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