Gain Agility with DevOps

Extend DevOps to the data warehouse


DevOps improves application quality and business agility by enabling smooth and efficient IT operations of continuously deployed application releases.  Under DevOps philosophy and practices, developers include enhancements in applications code to assist IT Operations when managing the applications in production. 

New communications is required between IT Operations and Application Development for continuous feedback to facilitate coding for operations.  The goal is smooth and efficient operations of incremental change and continuous deployment from an agile development process.

Extending DevOps to the Data Warehouse

Today’s leading companies are data driven.  Data and analytics drive most aspects of the company.  Decisions and actions are based on data.  Therefore, applications throughout the organization use shared corporate data as the basis of their functions.  As agile application development enables continuous deployment of incremental releases, the data and analytics required within the applications continuously changes as well.

Extending DevOps to the data warehouse enables:

  • Continuous deployment of changing query workloads
  • Data warehouse resource management
  • Data warehouse database and schema administration
  • Data warehouse performance management
  • Data/query user support and validation

Teradata Module for Python

The Teradata Module for Python is an open source client-side Python library for applications which use the Teradata Database. It adds code to build the operational information foundation for DevOps in the data warehouse while removing the application development tedium of database connectivity and coding to strict coding standards for operational logging and management.

The Teradata Python module offers:

  • Consistent application tooling and logging
  • Easy connection to the Teradata Database via Teradata REST Services or ODBC
  • Logging application session execution data in addition to individual query execution data
  • Version control
  • Python Data API specification v2.0 implementation
  • External configuration file to control logging level, target database (e.g., test, production), connection type, and more
  • Checkpoint restart of applications
  • Support for the following platforms
    • Python 2.7, Python 3.4+
    • Mac/Windows/Linux
    • 32-bit/64-bit

The Teradata Module for Python package is available and can be installed directly from PyPI.
The open source code is released to GitHub.
The documents are available on the Teradata Developer Exchange community site for Teradata Database.