No-code pipeline · Jira → BigQuery

Send data from Jira to BigQuery

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From Jira to BigQuery: managed, scheduled, no code.

Creating a pipeline that sends data from Jira to BigQuery data warehouses takes only a few minutes with Kondado. And the whole integration from Jira to BigQuery is managed and executed by our platform. With Kondado, you can focus on extracting value from Jira data and combining it with other data in your BigQuery data warehouse

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Send Jira Data to BigQuery Automatically

How to send Jira data to BigQuery? Kondado enables automated replication of your project management data from Jira to Google’s data warehouse without requiring any coding. You simply authenticate your Jira account as a data source, select BigQuery as your destination, and choose which pipelines to activate from the 13 available endpoints. The platform handles data extraction and loading automatically on your preferred schedule, whether you need updates every 5 minutes or daily batches, ensuring your analytics reflect current project status.

Once your Jira data lands in BigQuery, you can build custom reports combining sprint velocity, issue resolution times, and team workload metrics. Analysts query this information using standard SQL or connect business intelligence tools like Power BI and Looker Studio to visualize project health. This automated workflow eliminates manual CSV exports and keeps your project dashboards current with near-real-time data updates.

Kondado replicates Jira data to BigQuery on a configurable schedule, offering 13 pipelines including Issues, Boards, and Projects with 165 fields total, enabling automated analytics workflows without manual exports.

Our prices start from $19 USD/month, and you can try Kondado for free for 14 days with no credit card required

Need to track sprint performance across multiple teams? The Boards Sprints pipeline delivers sprint IDs, start dates, and states directly into your data warehouse, allowing you to calculate velocity trends and predict completion dates. Combine this with the Issues Change History pipeline to analyze how task priorities shift during development cycles, identifying bottlenecks that delay releases.

For project portfolio management, the Projects pipeline provides comprehensive metadata including project IDs, names, and types, while the Issues Links pipeline maps dependencies between tasks. In BigQuery, you can join these datasets to create network graphs showing critical path analysis or resource allocation matrices. This consolidated view enables data-driven decisions about team capacity and project timelines without switching between Jira interfaces.

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Replicated to BigQuery

Jira data available for BigQuery

Tables Kondado writes into your BigQuery, on a schedule you control.

available pipelines

165

extractable fields

BigQuery

Destination

Available integrations

Boards

Includes fields such as id, name, and type of the board, along with the associated project ID.

Boards Column Configuration

Presents fields such as column name, board ID, and status URL, allowing insight into the board's structure.

Boards Issues Relationship

Shows the relationship between issues and boards with fields such as issue ID and issue key.

Boards Sprints

Includes information about sprints, such as sprint ID, start date, and sprint state.

Issues

Contains fields such as issue ID, key, due date, and current status, enabling task management.

Issues Custom Fields

Allows the inclusion of custom fields for issues, facilitating the customization of information.

Issues Change History

Records changes with fields such as changelog ID, change date, and change author.

Issues Web Links

Includes links related to issues, allowing quick access to external resources.

Issues Activity Logs

Provides activity logs with fields such as issue ID and activity date, useful for auditing.

Issues Links

Presents links between issues, allowing visualization of relationships and dependencies between tasks.

Projects

Includes fields such as id, project name, and project type, enabling effective viewing and management of ongoing projects.

Sprints: relationship with issues

Presents fields such as sprint id, board id, and sprint state, facilitating the tracking of progress for related tasks.

Status

Contains information on id, status name, and description, allowing understanding of the current state of tasks and their lifecycle.

See all available data → Send Jira to BigQuery →

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How to send Jira data to BigQuery

Sync data automatically — no code, no manual exports.

Connect Jira as data source

Authenticate your Jira account in Kondado by providing your API credentials and selecting your site. The platform automatically detects available projects and validates access permissions to ensure smooth data extraction.

Configure BigQuery destination

Set up BigQuery as your target warehouse by specifying the dataset name and granting write permissions to Kondado. You can use an existing project or create a new dataset specifically for your Jira analytics.

Select pipelines and schedule

Choose which of the 13 available pipelines to activate, such as Issues, Boards Sprints, or Projects, then define your update frequency. Kondado will begin replicating data immediately and maintain automated refreshes according to your selected interval.

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Send data from Jira to other destinations

Jira > Looker Studio

Jira > Power BI

Other visualizations

Jira > Amazon S3

Jira > BigQuery

Jira > Excel

Jira > Google Sheets

Jira > MySQL

Jira > PostgreSQL

Jira > Redshift

Jira > SQL Server

Choose a tool to visualize your Jira data

If the software you need is not listed, drop us a messagem. You can use almost every tool

Jira > Airtable

Jira > Alteryx

Jira > Amazon Quicksight

Jira > BIMachine

Jira > Cortex

Jira > Data Studio

Jira > Geckoboard

Jira > Grafana

Jira > IBM Cognos Analytics

Jira > Klipfolio

Jira > Looker Studio

Jira > Looker

Jira > Looqbox

Jira > Metabase

Jira > Microstrategy

Jira > Pentaho

Jira > Power BI

Jira > Qlik Cloud Analytics

Jira > Qlik Sense

Jira > Qlik

Jira > QlikView

Jira > Redash

Jira > Superset

Jira > Tableau

Jira > BI TOTVS

Jira > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending Jira data to BigQuery automatically

How frequently does Kondado update Jira data in BigQuery? ▼

Kondado replicates data on a configurable schedule that you control, with options ranging from every 5 minutes to daily updates. You select the sync frequency during setup based on your analytics needs, ensuring your BigQuery datasets reflect recent project activities without overwhelming your API limits.

What specific Jira information can I analyze in BigQuery? ▼

You can access 13 different pipelines covering Boards, Issues, Sprints, and Projects with 165 total fields. This includes issue keys, sprint states, change histories, and custom fields, allowing you to build comprehensive reports on velocity, backlog health, and team productivity directly in BigQuery.

How is Jira data organized when it reaches BigQuery? ▼

Each activated pipeline becomes a separate table within your BigQuery dataset, maintaining relational structures that preserve connections between issues, boards, and sprints. The data arrives ready for SQL analysis, with consistent schemas that support joins across pipelines for complex project analytics.

Can I merge Jira data with other business systems in BigQuery? ▼

Yes, you can replicate data from additional sources like CRM or finance platforms into the same BigQuery project. This enables unified dashboards that correlate development metrics with sales pipelines or budget data using standard SQL joins across different datasets.

Does the Issues pipeline include custom field values from Jira? ▼

Standard issue fields like status and assignee come through the main Issues pipeline, while custom fields replicate separately through the Issues Custom Fields pipeline. This modular approach ensures your unique Jira configurations, such as story points or priority schemes, are available for analysis alongside standard task data.

How can I track issue dependencies in BigQuery? ▼

The Issues Links pipeline specifically captures relationships between tasks, including blocking and blocked dependencies. When combined with the Issues pipeline in BigQuery, you can query these connections to identify critical path items and assess the downstream impact of delayed tickets.

What historical data is available for Jira issues? ▼

The Issues Change History pipeline records every modification including status transitions, priority changes, and field updates with timestamps and author information. This allows you to calculate cycle times, analyze scope creep patterns, and audit project evolution over time using SQL queries in your data warehouse.

Try out all the features for free for 14 days

Related on Kondado

Try out all the features for free for 14 days