Python Data Loading from jira to snowflake using dlt Library

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The open-source Python library, dlt, facilitates the seamless transfer of data from Jira to Snowflake. Jira, a top-tier project management tool for agile teams, empowers users to plan, track, and release high-quality software. Its efficient task and project management features make the impossible, possible. On the other hand, Snowflake is a cloud-based data warehousing platform that specializes in storing, processing, and analyzing large volumes of data. By using dlt, you can effortlessly load data from Jira to Snowflake, enabling you to leverage Snowflake's robust data analysis capabilities on your Jira project data. For more details about Jira, visit https://www.atlassian.com/software/jira.

dlt Key Features

• Jira: Jira by Atlassian helps teams manage projects and tasks efficiently. This dlt verified source loads data using Jira API to the destination of your choice. Read more
• Snowflake: dlt supports Snowflake as a destination for data. It allows three types of authentication: password authentication, key pair authentication, and external authentication. Read more
• Data Lineage: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more
• Salesforce: Salesforce is a cloud platform that streamlines business operations and customer relationship management. This dlt verified source loads data using Salesforce API to the destination of your choice. Read more
• Data Extraction: Extracting data with dlt is simple - you simply decorate your data-producing functions with loading or incremental extraction metadata, which enables dlt to extract and load by your custom logic. 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 Snowflake:

pip install "dlt[snowflake]"

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

# create a new directory
mkdir my-jira-pipeline
cd my-jira-pipeline
# initialize a new pipeline with your source and destination
dlt init jira snowflake
# 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[snowflake]>=0.3.25

You now have the following folder structure in your project:

my-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:

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

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.snowflake.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!
warehouse = "warehouse" # please set me up!
role = "role" # please set me up!

Further help setting up your source and destinations

Please consult the detailed setup instructions for the Snowflake destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Jira source in the dlt verifed sources documentation.

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='snowflake', 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='snowflake',
        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 Snowflake 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 method uses a CI/CD runner that you can use for free. You need to specify when the GitHub Action should run using a cron schedule expression.
• Deploy with Airflow: You can deploy dlt using Airflow. This method involves creating an Airflow DAG for your pipeline script that you should customize. The DAG uses the dlt Airflow wrapper to make this process trivial.
• Deploy with Google Cloud Functions: dlt can also be deployed using Google Cloud Functions. This method involves deploying the pipeline to Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
• Other Deployment Methods: There are other methods to deploy dlt. You can find more information about these methods here.

The running in production section will teach you about:

• Monitor your Pipeline: Keep track of your pipeline's performance and status using dlt's monitoring capabilities. Learn more about how to monitor your pipeline.
• Set up Alerts: Stay informed about your pipeline's status with dlt's alerting feature. You can set up alerts to notify you of any changes or issues in your pipeline. Learn more about how to set up alerts.
• Trace your Pipeline: Trace your pipeline's execution to understand its performance and identify any potential issues. dlt provides tracing capabilities that allow you to track every step of your pipeline's execution. Learn more about how to set up tracing.

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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