Python-based dlt Library: Loading Data from jira to dremio

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This documentation provides guidance on using the open-source Python library, dlt, to manage data extraction from Jira and loading into Dremio. Jira is a leading project management tool for agile teams, enabling them to plan, track, and release world-class software. On the other hand, Dremio is a comprehensive data lakehouse solution offering flexibility, scalability, and performance for all stages of data management. By integrating dlt into your workflow, you can streamline the process of transporting data between Jira and Dremio. For more details about Jira, please visit https://www.atlassian.com/software/jira.

dlt Key Features

• Automated maintenance: dlt provides automated maintenance with schema inference and evolution and alerts. This feature, along with the short declarative code, simplifies the maintenance process. Read more
• Scalability and flexibility: dlt offers scalability through iterators, chunking, and parallelization techniques. It can run wherever Python runs - on Airflow, serverless functions, notebooks, and more. It doesn't require external APIs, backends, or containers, and it scales on micro and large infra alike. Read more
• Robust governance support: dlt pipelines offer robust governance support through 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. Read more
• User-friendly interface: dlt has a user-friendly, declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. It's easy to get started with dlt with the Getting started guide and Google Colab demo. Read more
• Community support: dlt has a supportive community where you can ask questions, share how you use the library, and report problems or make feature requests. Join the community on Slack or check out the code on GitHub. Read 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 Dremio:

pip install "dlt[dremio]"

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

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira dremio
# 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[dremio]>=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.dremio]
dataset_name = "dataset_name" # please set me up!
staging_data_source = "staging_data_source" # please set me up!

[destination.dremio.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 32010

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 Dremio 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='dremio', 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='dremio',
        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 Dremio 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 provides an easy way to deploy your pipeline with Github Actions. You just need to specify the schedule for the Github Action to run using a cron schedule expression.
• Deploy with Airflow: With dlt, you can easily deploy your pipeline with Airflow. It creates an Airflow DAG for your pipeline script that you can customize as per your needs.
• Deploy with Google Cloud Functions: dlt also allows you to deploy your pipeline with Google Cloud Functions. It provides step-by-step instructions on how you can do this.
• Other Deployment Options: Apart from the above, dlt supports several other deployment options. You can check out all the available options here.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides robust tools for monitoring your data pipeline, ensuring that you always have up-to-date information on its status. Learn how to effectively monitor your pipeline using dlt here.
• Set Up Alerts: Stay ahead of potential issues with dlt's alerting capabilities. You can set up alerts to notify you of any changes or problems in your pipeline. Find out how to set up alerts here.
• Implement Tracing: dlt makes it easy to trace your data pipeline, providing valuable insights into its operation and performance. Discover how to set up tracing in your pipeline 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 Name	Write Disposition	Description
issues	replace	Individual pieces of work to be completed. Contains various fields such as assignee, comments, created time, reporter, status, summary, updated time, etc.
projects	replace	A 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.
users	replace	Administrator of a given project. Contains fields such as account ID, account type, avatar URL, display name, email address, etc.
workflows	replace	The 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.

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