SAN DIEGO, Sept. 28, 2015 /PRNewswire/ -- To improve the agility of data analytics environments, Teradata (NYSE: TDC), the big data analytics and marketing applications company, today announced it is the first vendor to extend DevOps practices to the multi-application data warehouse environment through the launch of the open source Teradata Module for Python. The module enables software developers to create an entirely new wave of DevOps-enabled applications, which leverage data in the Teradata data warehouse.
The launch of open source Teradata Module for Python enables programmers and data scientists working in the ubiquitous Python language to easily create applications that exploit data in the Teradata Database, while enabling smoother data warehouse operations. Python applications can run on an application server and send SQL queries to the Teradata Database, or run within the Teradata Database. As in any Python application, programmers can use the vast collection of capabilities in Python libraries for advanced analytics or data manipulation. Publicly available Python libraries include the Python Standard Library, NumPy/SciPy, Biopython, Pandas, Mlpy, and Dateutil/Pytz.
Organizations often have hundreds or thousands of applications running thousands or millions of queries daily to meet the needs of frontline workers. The challenge is that applications are not static; they must constantly evolve to meet the ever-changing needs of the business. Teradata's introduction of DevOps provides a bridge between software developers and data warehouse operations, which enables them to easily create, continuously upgrade, and manage applications.
"By bringing DevOps practices to the data warehouse environment, Teradata has taken the lead to help customers to be more competitive with an agile data infrastructure that empowers the data-driven business," said Oliver Ratzesberger, president, Teradata Labs. "The open source Teradata Module for Python allows customers to easily build DevOps-enabled applications, which provide version control, configuration management and the logging of activity."
Teradata developed the Teradata Module for Python by leveraging the DevOps practices learned from its own product development and the most successful data warehouse users around the world. When developing applications with Teradata Module for Python, organizations don't need to recreate coding standards and tools for consistent operational logging to enable automated monitoring.
The Teradata Module for Python offers:
- Consistent application tooling and logging - Teradata Module for Python reduces the tedium of hand coding based on strict programming standards, and offers consistent activity logging and impact analysis capabilities.
- Easy connection to Teradata Database - The Python applications easily connect to the Teradata Database through Representational State Transfer (REST) services from any device, anytime and anywhere or standard ODBC (Open Database Connectivity) drivers.
- Application execution in addition to query execution – To support administrators overseeing operations, applications built in Python capture script version, run id, and execution time for version impact analysis and analyzing applications, not just queries.
- Python Database API Specification v2.0 – Conveniently implements the standard Python interface to databases.
"The introduction of DevOps practices for the data-driven business creates a new standard for agility, which is long overdue," said Stephen Hendrick, principal analyst, Application Development and Deployment Research, from ESG an IT research, analyst, strategy, and validation firm. "DevOps is generally discussed in a purely applications context, but rarely discussed as a part of a shared data warehouse environment. Teradata is uniquely positioned to help customers understand the interdependencies between applications and the data warehouse environment supporting continuous application deployment with continuously changing data and analytical requirements."
The Teradata Module for Python package is now available and can be installed directly from PyPI. The open source code is released to GitHub and the documents are available on the Teradata Developer Exchange community site for the Teradata Database. Follow Teradata's open source contribution on Teradata GitHub
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