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Python Guide: Loading Data from jira to microsoft sql server with dlt

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dlt allows Agile teams to streamline their project management by integrating Jira, a leading tool for planning, tracking, and releasing software, with Microsoft SQL Server, a robust relational database management system (RDBMS). By leveraging the capabilities of dlt, teams can efficiently load data from Jira into Microsoft SQL Server. This seamless integration facilitates communication via Transact-SQL, making it possible to perform tasks that were previously considered impossible. For more information on Jira, please visit https://www.atlassian.com/software/jira.

dlt Key Features​

  • Automated maintenance: dlt offers automated maintenance with schema inference and evolution and alerts, making maintenance simple. Check it out here.
  • Runs where Python runs: You can run dlt on Airflow, serverless functions, notebooks and more. No need for external APIs, backends or containers. Learn more here.
  • User-friendly, declarative interface: dlt is designed to be user-friendly and easy to understand, making it accessible for beginners and empowering for senior professionals. Read more here.
  • Getting started guide: Dive into the Getting started guide for a quick introduction to the essentials of dlt.
  • Google Colab demo: Play with the Google Colab demo to see dlt in action.

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 Microsoft SQL Server:

pip install "dlt[mssql]"

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

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira mssql
# 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[mssql]>=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.mssql.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 = 1433
connect_timeout = 15
driver = "driver" # 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 Microsoft SQL Server 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='mssql', 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='mssql',
        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 Microsoft SQL Server 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 can be deployed using GitHub Actions. This allows for continuous integration and delivery, enabling your pipeline to run on a schedule or in response to events. Learn how to deploy with GitHub Actions.
  • Deploy with Airflow and Google Composer: Airflow is a powerful tool for managing complex pipelines, and Google Composer provides a managed environment for running Airflow. dlt can be deployed using these tools, allowing for robust and scalable data processing. Learn how to deploy with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Google Cloud Functions provide a serverless environment for running your pipeline. This can be a cost-effective and scalable solution for deploying dlt. Learn how to deploy with Google Cloud Functions.
  • More Deployment Options: dlt provides flexibility in deployment options, allowing you to choose the best method for your needs. Explore other deployment methods and find the one that suits your project best.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to effectively monitor your pipeline, providing you with useful information on the recently loaded data and runtime trace from the pipeline. Learn more about monitoring your pipeline here.
  • Set Up Alerts: With dlt, you can set up alerts to be notified about any schema changes in your pipeline. This feature helps you to maintain your pipeline and adapt to any changes promptly. Learn how to set up alerts here.
  • Set Up Tracing: dlt offers tracing capabilities that provide timing information on extract, normalize, and load steps. It also contains all the config and secret values with full information from where they were obtained. Learn more about setting up tracing 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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