Using Python's dlt Library to Load Data from jira to aws s3

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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Welcome to our technical documentation on how to load data from jira to aws s3 using the dlt open source Python library. jira is a premier project management tool for agile teams, enabling them to plan, track, and release world-class software. On the other hand, aws s3 is a robust filesystem destination that stores data in remote file systems and bucket storages. It can be used as a staging area for other destinations or to quickly build a data lake. Our dlt library serves as the bridge between these two platforms, making the impossible, possible. For more information about jira, visit https://www.atlassian.com/software/jira.

dlt Key Features

• Jira Integration: dlt verified source for Jira helps in managing projects and tasks efficiently, prioritizing work, and collaborating by loading data using Jira API to the destination of your choice.

• Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts.

• Identifiers, Data Lineage, Schema Lineage: dlt provides identifiers for tracing data lineage and schema lineage, promoting better data management practices.

• Advanced Deployment Options: dlt offers advanced deployment options like using dlt init with branches, local folders or git repos.

• Filesystem & Buckets: dlt supports Filesystem & buckets to store data in remote file systems and bucket storages like S3, Google Storage or Azure Blob Storage.

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 my-jira-pipeline
cd my-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:

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.filesystem]
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!

Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 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='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: dlt provides a convenient command dlt deploy <script>.py github-action --schedule "*/30 * * * *" for deploying your pipeline as a Github Action. This allows you to automate your pipeline and schedule it to run at specific intervals. Learn more about this deployment method here.
• Deploy with Airflow: If you prefer using Airflow for orchestrating your data pipelines, dlt has got you covered. You can deploy your pipeline to Airflow using the command dlt deploy <script>.py airflow-composer. Detailed instructions on how to do this can be found here.
• Deploy with Google Cloud Functions: dlt also supports deploying your pipeline as a Google Cloud Function. This allows your pipeline to be triggered by specific events in Google Cloud. Read more about deploying with Google Cloud Functions here.
• Other Deployment Methods: Apart from the above mentioned methods, dlt supports various other deployment methods. You can explore all the deployment methods supported by dlt here.

The running in production section will teach you about:

• Monitoring your pipeline: dlt provides a comprehensive suite of monitoring tools to help you keep an eye on your pipeline's performance and catch any potential issues early. Learn more about how to monitor your pipeline here.
• Setting up alerts: With dlt, you can set up alerts to notify you of any important events or potential issues with your pipeline. This feature allows you to respond quickly to any problems and keep your pipeline running smoothly. Find out how to set up alerts here.
• Enabling tracing: Tracing is a powerful tool that dlt offers to help you track the execution of your pipeline and identify any bottlenecks or inefficiencies. Learn more about 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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