Getting Started with the CData Connect AI Python SDK for Jira
The CData Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client that lets you fetch and act on live Jira data with standard Python database code. Because Connect AI provides the connectivity: you install one package, authenticate with a Personal Access Token, and query Jira (and every other source connected in Connect AI) using the same familiar connect() / cursor() / fetchall() pattern you already know from libraries like sqlite3 and psycopg2.
This guide walks through connecting Jira in Connect AI, generating a Personal Access Token, installing the SDK, and reading (and, where supported, writing) live Jira data.
Prerequisites
- An account in CData Connect AI
- Python 3.8 or higher
- An active Jira account with valid credentials
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.
- Log into Connect AI, click Sources, and then click Add Connection
- Select "Jira" from the Add Connection panel
-
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.
- Click Save & Test
- Navigate to the Permissions tab and update the user-based 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.
- Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
- On the Settings page, go to the Access Tokens section and click Create PAT.
- Give the PAT a name and click Create.
- The PAT is only visible at creation, so copy it and store it securely.
Install the SDK
Install the SDK from PyPI with pip:
pip install cdata-connect-ai
Connect and Run Your First Query
Connect with your account email and PAT, then query sys_tables to discover every table available across your connected sources. Identifiers in Connect AI are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, JIRA1).
import cdata_connect_ai
conn = cdata_connect_ai.connect(
username="[email protected]",
password="<your_pat>",
)
cur = conn.cursor()
# Discover what's available across your connected sources
cur.execute("SELECT CatalogName, SchemaName, TableName FROM sys_tables LIMIT 25")
for row in cur.fetchall():
print(row)
Pick any table from the results and query it directly:
cur.execute(
"SELECT Summary, TimeSpent "
"FROM [JIRA1].[JIRA].[Issues] "
"LIMIT 10"
)
for row in cur.fetchall():
print(row)
Jira is a read-only source in Connect AI, so the SDK supports queries but not INSERT, UPDATE, or DELETE. Close the connection when you are finished:
conn.close()
That is the entire workflow: one package, a PAT, and standard DB-API calls. Because the SDK returns a normal DB-API connection, it drops straight into the rest of the Python data ecosystem. From here you can load Jira data into pandas, build ETL pipelines with petl, or power a Dash web app, all using this same connection.
More Information and Free Trial
Now you can query live Jira data from Python through 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 working with live Jira data in Python.