Use Dash to Build Web Apps on Jira Data via CData Connect AI

Jerod Johnson
Jerod Johnson
Director, Technology Evangelism
Build interactive Dash web apps on live Jira data using pandas and the CData Connect AI Python SDK.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas module, and the Dash framework, you can build Jira-connected web applications for Jira data. This article shows how to connect to Connect AI and use pandas and Dash to build a simple web app for visualizing Jira data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source: connect with a Personal Access Token and build your app.

About Jira Data Integration

CData simplifies access and integration of live Jira data. Our customers leverage CData connectivity to:

  • Gain bi-directional access to their Jira objects like issues, projects, and workflows.
  • Use SQL stored procedures to perform functional actions like changing issues status, creating custom fields, download or uploading an attachment, modifying or retrieving time tracking settings, and more.
  • Authenticate securely using a variety of methods, including username and password, OAuth, personal access token, API token, Crowd or OKTA SSO, LDAP, and more.

Most users leverage CData solutions to integrate Jira data with their database or data warehouse, whether that's using CData Sync directly or relying on CData's compatibility with platforms like SSIS or Azure Data Factory. Others are looking to get analytics and reporting on live Jira data from preferred analytics tools like Tableau and Power BI.

Learn more about how customers are seamlessly connecting to their Jira data to solve business problems from our blog: Drivers in Focus: Collaboration Tools.


Getting Started


Connect to Jira in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. Adding a Connection
  3. Select "Jira" from the Add Connection panel
  4. Selecting a data source
  5. Enter the necessary authentication properties to connect to Jira.

    To connect to JIRA, provide the User and Password. Additionally, provide the Url; for example, https://yoursitename.atlassian.net.

    Configuring a connection (Salesforce is shown)
  6. Click Save & Test
  7. Navigate to the Permissions tab and update the user-based permissions. Updating permissions

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

  1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
  2. On the Settings page, go to the Access Tokens section and click Create PAT.
  3. Give the PAT a name and click Create. Creating a new PAT
  4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra), Dash, and Plotly using the pip utility:

pip install "cdata-connect-ai[full]"
pip install dash
pip install plotly

Build a Web App on Jira Data in Python

Once the required modules are installed, you are ready to build the web app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules, then connect to Connect AI with your account email and PAT and read Jira data into a DataFrame. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, JIRA1).

import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pd.read_sql(
    "SELECT Summary, TimeSpent "
    "FROM [JIRA1].[JIRA].[Issues] "
    "WHERE ReporterDisplayName = 'Bob'",
    conn,
)

conn.close()

Configure the App and Layout

With the query results stored in a DataFrame, build a bar graph from the Jira data and configure the app layout.

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['Summary'], y=df['TimeSpent'], name='Summary')

app.layout = html.Div(
    children=[
        html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
        dcc.Graph(
            id='example-graph',
            figure={
                'data': [trace],
                'layout': go.Layout(title='Jira Issues Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

Set the App to Run

With the connection, app, and layout configured, you are ready to run the app.

if __name__ == '__main__':
    app.run(debug=True)

Now, use Python to run the web app and a browser to view the Jira data.

python jira-dash.py
The Dash web app running in a browser (Salesforce is shown)

More Information and Free Trial

Now you can build interactive Dash web apps on live Jira data using the CData Connect AI Python SDK. For more information on connecting to Jira (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building web apps on live Jira data in Python.



Full Source Code

import dash
from dash import dcc, html
import pandas as pd
import plotly.graph_objs as go
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pd.read_sql(
    "SELECT Summary, TimeSpent "
    "FROM [JIRA1].[JIRA].[Issues] "
    "WHERE ReporterDisplayName = 'Bob'",
    conn,
)

conn.close()

app = dash.Dash(__name__)
app.title = 'CData Connect AI + Dash'

trace = go.Bar(x=df['Summary'], y=df['TimeSpent'], name='Summary')

app.layout = html.Div(
    children=[
        html.H1("CData Connect AI + Dash", style={'textAlign': 'center'}),
        dcc.Graph(
            id='example-graph',
            figure={
                'data': [trace],
                'layout': go.Layout(title='Jira Issues Data', barmode='stack'),
            },
        ),
    ],
    className="container",
)

if __name__ == '__main__':
    app.run(debug=True)

Ready to get started?

Learn more about CData Connect AI or sign up for free trial access:

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