No-code pipeline · Jira → Amazon S3

Send data from Jira to Amazon S3

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From Jira to Amazon S3: managed, scheduled, no code.

Creating a pipeline that sends data from Jira to Amazon S3 data lakes takes only a few minutes with Kondado. And the whole integration from Jira to Amazon S3 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 Amazon S3 data lake

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Send Jira Data to Amazon S3 Automatically

Connect your Jira account to Kondado as a data source, select the pipelines you need from the available 13 endpoints covering boards, issues, and projects, and configure Amazon S3 as your destination. Kondado replicates your Jira data on a configurable schedule, delivering fresh files to your S3 bucket every 5 minutes, hourly, or daily based on your business needs. This automated process eliminates manual exports and ensures your AWS data lake always contains current project management data for analysis.

Kondado provides a direct integration between Jira and Amazon S3, replicating 13 pipelines with 165 fields including Issues, Boards, Sprints, and Projects on a user-configured schedule ranging from 5 minutes to daily intervals.

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

What You Can Do with Jira Data in Amazon S3

Replicate the Issues pipeline to capture task IDs, due dates, and status changes directly into your S3 bucket, enabling detailed sprint retrospectives and workload analysis using Athena or Presto. The Boards and Boards Sprints pipelines provide structural context for your Kanban and Scrum workflows, allowing you to correlate board configurations with sprint velocity and project timelines. Once stored in Amazon S3, this Jira data combines seamlessly with other business data to power custom analytics and reporting workflows across your organization.

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Replicated to Amazon S3

Jira data available for Amazon S3

Tables Kondado writes into your Amazon S3, on a schedule you control.

available pipelines

165

extractable fields

Amazon S3

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 Amazon S3 →

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How to send Jira data to Amazon S3

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. This establishes the connection needed to access your boards, issues, and project data.

Configure Amazon S3 Destination

Enter your AWS credentials and specify the S3 bucket and folder path where you want Jira files stored. Kondado validates the connection to ensure your data lands in the correct location within your AWS infrastructure.

Select Pipelines and Schedule

Choose from the 13 available pipelines such as Issues, Boards, and Sprints, then set your preferred update frequency from 5 minutes to daily. This determines how often fresh data flows into your S3 bucket for analysis.

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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 Amazon S3 automatically

How does Kondado replicate Jira data to Amazon S3? ▼

Kondado connects to your Jira instance using a direct integration, extracting data from your selected pipelines and loading it into your specified S3 bucket. The platform handles the data transformation and scheduling automatically, delivering structured files ready for querying with tools like Athena or Dremio.

What Jira pipelines are available for replication to S3? ▼

Kondado offers 13 pipelines including Issues, Boards, Sprints, Projects, and Issues Change History, covering 165 total fields. These pipelines capture everything from task details and custom fields to sprint states and board configurations, providing comprehensive coverage of your agile project management data.

How often does Jira data update in my S3 bucket? ▼

You configure the update frequency based on your needs, choosing intervals from every 5 minutes to daily schedules. This flexibility ensures your Amazon S3 data lake reflects recent project changes without overwhelming your storage with unnecessary updates.

What file format does Jira data arrive in when sent to Amazon S3? ▼

Data arrives in optimized file formats suitable for analytical querying, typically Parquet or JSON, depending on your configuration. These formats integrate directly with AWS Athena, Presto, and other data virtualization tools commonly used with S3 storage.

Can I combine Jira data with other sources in my S3 data lake? ▼

Yes, you can replicate data from multiple sources into the same S3 bucket or prefix, creating a unified data lake for cross-functional analysis. Combine Jira project data with information from BigQuery exports, PostgreSQL databases, or other tools to build comprehensive reports.

Do I need coding skills to set up Jira to S3 replication? ▼

No coding is required to configure the connection, select your pipelines, and schedule updates through Kondado's interface. The no-code setup process allows both technical teams and business analysts to begin replicating data within minutes.

Which AWS analytics tools work best with replicated Jira data? ▼

Replicated Jira data in S3 works optimally with AWS Athena for SQL querying, Presto for distributed analytics, and Dremio for data virtualization. You can also send data to Power BI or Looker Studio if you prefer visualization platforms outside the AWS ecosystem.

Try out all the features for free for 14 days

Related on Kondado

Try out all the features for free for 14 days