Python dlt Library: Loading Data from jira to redshift Guide

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This technical documentation provides a guide on how to leverage the dlt open source Python library to load data from Jira, a top-tier project management tool for agile teams, into Redshift, Amazon's fully managed, petabyte-scale data warehouse service. Jira empowers teams to plan, track, and release world-class software, making the impossible, possible. On the other hand, Redshift offers scalability from a few hundred gigabytes to a petabyte or more. The dlt library serves as a bridge, enabling seamless data transfer between these two platforms. For more information on Jira, visit https://www.atlassian.com/software/jira.

dlt Key Features

• Jira Integration: dlt integrates with Jira, allowing teams to manage projects and tasks efficiently, prioritize work, and collaborate effectively. This verified source supports endpoints such as issues, users, workflows, and projects.
• Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts.
• Amazon Redshift: dlt supports Amazon Redshift as a destination. It provides a detailed setup guide, including installing the necessary dependencies, setting up the Redshift cluster, and adding the necessary credentials.
• Zendesk: dlt integrates with Zendesk, a cloud-based customer service and support platform. This verified source supports multiple endpoints, including "Zendesk Support API", "Zendesk Chat API" and "Zendesk Talk API".
• Salesforce: dlt integrates with Salesforce, a cloud platform that streamlines business operations and customer relationship management. This verified source supports multiple resources, including User, UserRole, Lead, Contact, Campaign, Product2, Pricebook2, PricebookEntry, Opportunity, OpportunityLineItem, OpportunityContactRole, Account, CampaignMember, Task, and Event.

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 Redshift:

pip install "dlt[redshift]"

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

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira redshift
# 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[redshift]>=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.redshift.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!
port = 5439
connect_timeout = 15

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 Redshift 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='redshift', 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='redshift',
        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 Redshift 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 command to prepare your pipeline for deployment using Github Actions. This CI/CD runner is basically free to use and allows you to specify when the action should run using a cron schedule expression.
• Deploy with Airflow: You can also deploy your pipeline using Airflow. dlt command will create an Airflow DAG for your pipeline script that you should customize. This method is especially useful for Google Composer users.
• Deploy with Google Cloud Functions: dlt supports deployment with Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
• Other Deployment Options: Apart from the above, dlt supports various other deployment options. You can explore these options here.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides robust monitoring capabilities that enable you to keep a close eye on your pipeline's performance and status. This includes tracking the status of jobs, inspecting load packages, and saving load information for future reference. Learn more about it here.
• Set Up Alerts: Stay informed of any issues or changes in your pipeline with dlt's alerting feature. This allows you to set up notifications for schema changes, failed jobs, and other important events. Find out how to set up alerts here.
• Enable Tracing: dlt allows you to trace the runtime of your pipeline, providing valuable insights into the extract, normalize, and load steps. This feature also includes information on config and secret values, making it easier to debug and optimize your pipeline. Read more about setting 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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