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Loading Jira Data to AWS Athena Using Python and dlt Library

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dlt is an open-source Python library that simplifies the process of loading data from different sources into various destinations. This technical documentation provides information on how to use dlt to load data from Jira, a leading project management tool for agile teams, into AWS Athena, an interactive query service that allows easy analysis of data in Amazon S3 using standard SQL. The guide covers how to plan, track, and release world-class software in Jira and how to analyze the resulting data in AWS Athena which also supports iceberg tables. For more details on Jira, visit Atlassian's website.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines use metadata to offer governance capabilities. This includes load IDs for tracking data loads and enabling data lineage and traceability. More details can be found here.
  • Schema Enforcement and Curation: The library allows users to enforce and curate schemas, ensuring data consistency and quality. Schemas guide the processing and loading of data, maintaining data integrity and promoting standardized data handling practices. Read more here.
  • Schema Evolution: dlt alerts users to schema changes. When the source data’s schema undergoes modifications, dlt notifies stakeholders, allowing them to take necessary actions. This contributes to better data management practices and overall data governance. More information can be found here.
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines. This includes running extraction, normalization and load in parallel, writing sources and resources that run in parallel via thread pools and async execution, and finetuning the memory buffers, intermediary file sizes and compression options. Learn more about performance.
  • Community Support: dlt is a constantly growing library supported by a community of users. Join the Slack community to find recent releases or discuss what you can build with dlt.

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 AWS Athena:

pip install "dlt[athena]"

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

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

[destination.athena.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # 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 AWS Athena 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='athena', 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='athena',
        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 AWS Athena 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. Github Actions is a CI/CD runner that can be used for free. You can specify when the Github Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy dlt using Airflow. Google Composer, a managed Airflow environment provided by Google, can be used for this purpose. The deployment process involves creating an Airflow DAG for your pipeline script.
  • Deploy with Google Cloud Functions: dlt can be deployed using Google Cloud Functions. This allows you to deploy your pipeline in a serverless environment, reducing the need for server management.
  • Other Deployment Methods: There are various other methods to deploy dlt. You can explore them here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides a wide range of options for monitoring your data pipeline, including inspecting load info and traces, as well as schema changes. You can find more details on how to monitor your pipeline here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any changes or issues in your pipeline. This feature allows you to maintain a high level of control and awareness over your data pipeline. Learn more about setting up alerts here.
  • Set Up Tracing: dlt allows you to set up tracing for your data pipeline, providing you with detailed insights into the performance and behavior of 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 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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