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Load Data from jira to duckdb using Python and dlt Library

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This page provides technical documentation on how to utilize the open-source Python library, dlt, to load data from Jira into DuckDB. Jira, a premier project management tool for agile teams, allows for the planning, tracking, and releasing of world-class software. On the other hand, DuckDB is a swift in-process analytical database with a feature-rich SQL dialect and deep integrations into client APIs. By leveraging dlt, you can bridge these two powerful tools, making the impossible, possible. For further information on Jira, visit https://www.atlassian.com/software/jira.

dlt Key Features​

  • Jira: dlt supports Jira as a verified source, enabling teams to manage projects and tasks efficiently, prioritize work, and collaborate. The data from Jira can be loaded using the Jira API to the destination of your choice.

  • Detailed Tutorial: dlt provides a comprehensive tutorial that guides users to build a data pipeline that loads data from the GitHub API into DuckDB. The tutorial covers several important features of dlt and provides a step-by-step guide to using them.

  • DuckDB Support: DuckDB is a supported destination in dlt. Users can load data into DuckDB using large INSERT VALUES statements by default. The library also supports different file formats to load data into DuckDB.

  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance.

  • Loading Data from an API: dlt provides a simple process to retrieve and load data from an API into a destination like DuckDB. The tutorial provides a step-by-step guide to create a pipeline, run it, and inspect the loaded data.

Getting started with your pipeline locally​

0. Prerequisites​

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt​

First you need to install the dlt library with the correct extras for DuckDB:

pip install "dlt[duckdb]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Jira to DuckDB. You can run the following commands to create a starting point for loading data from Jira to DuckDB:

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira duckdb
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:

dlt[duckdb]>=0.3.25

You now have the following folder structure in your project:

jira_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── jira/                   # folder with source specific files
│   └── ...
├── jira_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials​

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml​

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.jira]
subdomain = "subdomain" # please set me up!
email = "email" # please set me up!
api_token = "api_token" # please set me up!

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the Jira source in our docs.
  • Read more about setting up the DuckDB destination in our docs.

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at jira_pipeline.py, as well as a folder jira that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:


from typing import List, Optional

import dlt
from jira import jira, jira_search


def load(endpoints: Optional[List[str]] = None) -> None:
    """
    Load data from specified Jira endpoints into a dataset.

    Args:
        endpoints: A list of Jira endpoints. If not provided, defaults to all resources.
    """
    if not endpoints:
        endpoints = list(jira().resources.keys())

    pipeline = dlt.pipeline(
        pipeline_name="jira_pipeline", destination='duckdb', dataset_name="jira"
    )

    load_info = pipeline.run(jira().with_resources(*endpoints))

    print(f"Load Information: {load_info}")


def load_query_data(queries: List[str]) -> None:
    """
    Load issues from specified Jira queries into a dataset.

    Args:
        queries: A list of JQL queries.
    """
    pipeline = dlt.pipeline(
        pipeline_name="jira_search_pipeline",
        destination='duckdb',
        dataset_name="jira_search",
    )

    load_info = pipeline.run(jira_search().issues(jql_queries=queries))

    print(f"Load Information: {load_info}")


if __name__ == "__main__":
    # Add your desired endpoints to the list 'endpoints'
    load(endpoints=None)

    queries = [
        "created >= -30d order by created DESC",
        'project = KAN AND status = "In Progress" order by created DESC',
    ]

    load_query_data(queries=queries)

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python jira_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline jira_pipeline info

You can also use streamlit to inspect the contents of your DuckDB destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline jira_pipeline show

5. Next steps to get your pipeline running in production​

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to​

  • Deploy with Github Actions: dlt supports deployment through Github Actions. This is a CI/CD runner that you can use basically for free.
  • Deploy with Airflow: You can also deploy dlt using Airflow. This method utilizes Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: dlt can be deployed using Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with your code.
  • Other Deployment Methods: dlt supports various other deployment methods. You can explore more about them here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to keep a close eye on your pipeline's performance and health. You can find out how to set this up here.
  • Set Up Alerts: With dlt, you can receive notifications about any significant events or issues in your pipeline. Learn how to configure these alerts here.
  • Enable Tracing: dlt provides powerful tracing capabilities to help you debug and optimize your pipeline. Find out how to enable this feature here.

Available Sources and Resources​

For this verified source the following sources and resources are available

Source jira​

The Jira source provides data on project management tasks, including details on issues, users, workflows, and projects.

Resource NameWrite DispositionDescription
issuesreplaceIndividual pieces of work to be completed. Contains various fields such as assignee, comments, created time, reporter, status, summary, updated time, etc.
projectsreplaceA collection of tasks that need to be completed to achieve a certain outcome. Contains fields such as avatar URL, description, ID, key, lead, name, etc.
usersreplaceAdministrator of a given project. Contains fields such as account ID, account type, avatar URL, display name, email address, etc.
workflowsreplaceThe key aspect of managing and tracking the progress of issues or tasks within a project. Contains fields such as created time, description, ID, updated time, etc.

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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