It may seem like the desires for end-to-end DevOps and protection of sensitive data are in conflict, but if done correctly, they can be two sides of the same coin. DevOps processes such as version control and delivery automation introduce the very measures needed to properly protect production data. The key to keeping data safe while using it during your DevOps process is to focus on these four areas.
Yaniv Yehuda details how DevOps is a natural evolution within the software industry as it drives business value and enables the organization. This article will describe how database management and the database administrators need to be part of any comprehensive DevOps approach.
Scott Ambler explains how DevOps has grown within the agile community, and why he believes it will become an IT buzzword in 2012. DevOps uses agile's community-based teamwork and offers developers and those in operations a great way to make everyone's job easier.
In this interview, Cher Fox, of Fox Consulting, explains why test automation is essential for agile data teams' success. However, there are many other items to consider and address before implementing test automation. You may be able to get started with tools you already have.
Data is the most valuable commodity in the world, and testers generate the most valuable data in the product development organization. When effectively tracked and presented, that data can inform release schedules, aid in decision-making, and shape the direction of the product.
This paper sets out to illustrate some of the ways that data can influence the test process, and will show that testing can be improved by a careful choice of input data. In doing this, the paper will concentrate most on data-heavy applications; those which use databases or are heavily influenced by the data they hold. The paper will focus on input data, rather than output data or the transitional states the data passes through during processing, as input data has the greatest influence on functional testing and is the simplest to manipulate. The paper will not consider areas where data is important to non-functional testing, such as operational profiles, massive datasets and environmental tuning.