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Python dlt Library: Loading jira Data into aws s3

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dlt is an open-source Python library that simplifies data loading from various sources to different destinations. This documentation focuses on using dlt to transfer data from Jira, a leading project management tool for agile teams, to AWS S3, a robust filesystem destination ideal for creating datalakes. Jira enables teams to plan, track, and release world-class software, making the impossible, possible. In contrast, AWS S3 supports data storage in various formats like JSONL, Parquet, or CSV. By integrating these tools with dlt, users can efficiently manage data transfer processes. For more details about Jira, visit https://www.atlassian.com/software/jira.

dlt Key Features​

  • Jira Integration: dlt provides a verified source for Jira, allowing you to manage and track projects and tasks efficiently.
  • Filesystem & Buckets: dlt supports data storage in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. Find out more about Filesystem & Buckets here.
  • Provider Key Formats: dlt supports different formats for the keys. It translates the standard format where sections and key names are separated by "." into the provider-specific formats. Learn more about Provider Key Formats here.
  • Salesforce Integration: dlt provides a verified source for Salesforce, a cloud platform that streamlines business operations and customer relationship management.
  • Detailed Tutorials: dlt provides detailed tutorials and guides to help you create and run effective data pipelines. Check out the tutorial 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 AWS S3:

pip install "dlt[filesystem]"

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 S3. You can run the following commands to create a starting point for loading data from Jira to AWS S3:

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

[destination.filesystem.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 S3 destination in our docs.

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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='filesystem', 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='filesystem',
        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 S3 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: Learn how to set up and deploy your dlt pipeline using GitHub Actions for a CI/CD workflow. Follow the step-by-step guide here.
  • Deploy with Airflow and Google Composer: Discover how to deploy your dlt pipeline using Airflow and Google Composer. Detailed instructions can be found here.
  • Deploy with Google Cloud Functions: Find out how to deploy your dlt pipeline using Google Cloud Functions for serverless execution. Check the guide here.
  • Other Deployment Options: Explore various other methods to deploy your dlt pipeline, including different cloud services and environments. More information is available here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipelines to ensure they are running smoothly and efficiently. How to Monitor your pipeline
  • Set up alerts: Configure alerts to get notified about important events or issues in your dlt pipelines. Set up alerts
  • Set up tracing: Implement tracing to keep track of the execution and performance of your dlt pipelines. And 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 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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