Python dlt Library: Loading Data from jira to databricks

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This documentation provides a guide on how to use dlt, an open-source Python library, to load data from Jira into Databricks. Jira, a leading project management tool for agile teams, enables planning, tracking, and releasing world-class software. On the other hand, Databricks, created by the original authors of Apache Spark™, is a unified data analytics platform that merges data science, engineering, and business. With dlt, you can bridge these two powerful platforms, making it possible to efficiently manage and analyze your project data. For more information about Jira, visit https://www.atlassian.com/software/jira.

dlt Key Features

• Jira Verified Source: dlt provides a verified source for Jira, allowing efficient management of projects and tasks, prioritizing work, and collaboration. The source supports various endpoints such as issues, users, workflows, and projects.

• Governance Support: dlt pipelines offer robust governance support through key mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about lineage, Schema adjustment, and performance.

• Identifiers, Data Lineage and Schema Lineage: dlt provides identifiers, data lineage, and schema lineage to maintain data consistency, traceability, and control throughout the data processing lifecycle. Learn more here.

• Getting Started with dlt: dlt provides a Getting started guide, a Google Colab demo, a Tutorial, and How-to guides to help users understand how to build a pipeline that loads data from an API.

• Data Extraction with dlt: dlt simplifies data extraction by leveraging iterators, chunking, and parallelization techniques. It also utilizes implicit extraction DAGs for efficient API calls for data enrichments or transformations. Learn more 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 Databricks:

pip install "dlt[databricks]"

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

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira databricks
# 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[databricks]>=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.databricks.credentials]
catalog = "catalog" # please set me up!
server_hostname = "server_hostname" # please set me up!
http_path = "http_path" # please set me up!
access_token = "access_token" # 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 Databricks 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='databricks', 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='databricks',
        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 Databricks 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: You can deploy your dlt pipeline using Github Actions. This CI/CD runner is basically free and you can specify when the Github Action should run using a cron schedule expression.
• Deploy with Airflow: Airflow is another option for deploying your dlt pipeline. It's a managed environment provided by Google that creates an Airflow DAG for your pipeline script.
• Deploy with Google Cloud Functions: If you prefer, you can also deploy your dlt pipeline with Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
• Other Deployment Options: There are many other ways to deploy your dlt pipeline. You can explore more options here.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt allows you to monitor your pipeline in real-time, providing you with valuable insights about your data loading process. Check out the guide on how to monitor your pipeline.
• Set Up Alerts: With dlt, you can set up alerts to notify you of any issues with your pipeline. This ensures that you are always aware of the status of your pipeline and can take action if necessary. Learn more about how to set up alerts.
• Set Up Tracing: dlt also allows you to set up tracing, which can help you understand the performance of your pipeline and identify any potential bottlenecks. Discover 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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