Python Data Loading from jira to azure synapse using dlt

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This page provides technical documentation on how to load data from jira, an industry-leading project management tool for agile teams, into azure synapse, a comprehensive analytics service combining enterprise data warehousing and Big Data analytics. This process is facilitated by dlt, an open-source Python library. The goal is to make the seemingly impossible, possible by leveraging the power of these three platforms. jira allows for efficient planning, tracking, and releasing of software, while azure synapse provides limitless analytics capabilities. With dlt, these two can seamlessly integrate, enabling you to maximize your data's potential. For more details about jira, visit Atlassian's official website.

dlt Key Features

• Jira Verified Source: The Jira verified source by Atlassian helps teams manage projects and tasks efficiently, prioritize work, and collaborate.
• Azure Synapse Destination: The Azure Synapse is a dlt destination that allows you to manage and analyze your data effectively.
• Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about it here.
• Asana Verified Source: The Asana verified source is a web-based project management and collaboration tool that helps teams stay organized, focused, and productive.
• Advanced Usage: dlt provides advanced features like using dlt init with branches, local folders or git repos. You can find out more about this here.

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 Azure Synapse:

pip install "dlt[synapse]"

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

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira synapse
# 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[synapse]>=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.synapse]
create_indexes = false
default_table_index_type = "heap"
staging_use_msi = false

[destination.synapse.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 Azure Synapse 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='synapse', 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='synapse',
        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 Azure Synapse 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 pipeline using Github Actions. This is a CI/CD runner that you can use for free. You can specify when the GitHub Action should run using a cron schedule expression. Learn more
• Deploy with Airflow: dlt also provides the option to deploy your pipeline with Airflow. It creates an Airflow DAG for your pipeline script that you should customize. The DAG uses dlt Airflow wrapper to make this process trivial. Learn more
• Deploy with Google Cloud Functions: You can also deploy your pipeline with Google Cloud Functions using dlt. This allows you to run your code in response to events without having to manage the underlying infrastructure. Learn more
• Other Deployment Options: dlt supports many other deployment options as well. You can choose the one that best suits your needs. Learn more

The running in production section will teach you about:

• Monitor your pipeline: Understanding the performance and health of your pipeline is crucial when running in production. dlt provides multiple ways to monitor your pipeline, including logging, metrics, and tracing. Learn more about how to monitor your pipeline here.
• Set up alerts: Setting up alerts is a proactive way to identify issues in your pipeline before they become critical. dlt supports various alerting mechanisms, allowing you to be notified of any issues in real-time. Find out how to set up alerts here.
• Enable tracing: Tracing allows you to track the execution of your pipeline, providing insights into the performance and potential bottlenecks. dlt supports tracing, making it easy to understand the flow of data through your pipeline. Discover how to set 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 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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