Skip to main content

Load Data from jira to motherduck using Python dlt Library

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

This guide provides a walkthrough on how to utilize dlt, an open-source Python library, to load data from Jira into MotherDuck. Jira is a leading project management tool for agile teams, allowing for efficient planning, tracking, and releasing of software. On the other hand, MotherDuck is a fast, in-process analytical database with deep integrations into client APIs. By leveraging the capabilities of dlt, users can bridge the gap between these two platforms, making the impossible, possible. For more information on Jira, visit https://www.atlassian.com/software/jira.

dlt Key Features​

  • Fetching data from the GitHub API: Learn how to use dlt to fetch data from APIs and load it into your data warehouse. Learn more

  • Understanding and managing data loading behaviors: dlt provides flexibility in how you load and manage your data. Learn how to append or replace your data to suit your needs. Learn more

  • Incrementally loading new data and deduplicating existing data: dlt supports incremental loading of new data and deduplication of existing data. This feature ensures that your data is always up-to-date and clean. Learn more

  • Securely handling secrets: dlt provides a secure way to handle secrets, ensuring the safety of your sensitive data. Learn more

  • Making reusable data sources: With dlt, you can create reusable data sources to streamline your data pipeline and reduce code redundancy. Learn more

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 MotherDuck:

pip install "dlt[motherduck]"

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 MotherDuck. You can run the following commands to create a starting point for loading data from Jira to MotherDuck:

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira motherduck
# 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[motherduck]>=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!

[destination.motherduck.credentials]
database = "database" # please set me up!
password = "password" # 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 MotherDuck 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='motherduck', 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='motherduck',
        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 MotherDuck 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 allows you to deploy your pipelines with Github Actions. It provides step by step instructions on how to prepare your pipeline for deployment and how to schedule the Github Action. For more information, visit Github Actions
  • Deploy with Airflow: With dlt, you can also deploy your pipelines with Airflow. This includes using Google Composer, a managed Airflow environment provided by Google. For detailed instructions, visit Airflow
  • Deploy with Google Cloud Functions: dlt also supports deploying your pipelines with Google Cloud Functions. This allows you to execute your pipeline code in response to events without having to manage any server infrastructure. For more details, visit Google cloud functions
  • Other Deployment Options: dlt supports various other deployment options as well. For a comprehensive list and detailed instructions, visit and others...

The running in production section will teach you about:​

  • Monitor your pipeline: You can monitor your pipeline's performance and track its progress by using various tools and features provided by dlt. Learn more about it here.
  • Set up alerts: Alerts are essential to keep you informed about any issues or changes in your pipeline. dlt allows you to easily set up alerts. Check out the guide here.
  • Set up tracing: Tracing helps you to understand the execution flow of your pipeline and identify any bottlenecks or errors. Learn how to set up tracing in dlt 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!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.