Book Image

Testing Practitioner Handbook

By : Renu Rajani
Book Image

Testing Practitioner Handbook

By: Renu Rajani

Overview of this book

The book is based on the author`s experience in leading and transforming large test engagements and architecting solutions for customer testing requirements/bids/problem areas. It targets the testing practitioner population and provides them with a single go-to place to find perspectives, practices, trends, tools, and solutions to test applications as they face the evolving digital world. This book is divided into five parts where each part explores different aspects of testing in the real world. The first module explains the various testing engagement models. You will then learn how to efficiently test code in different life cycles. The book discusses the different aspects of Quality Analysis consideration while testing social media, mobile, analytics, and the Cloud. In the last module, you will learn about futuristic technologies to test software. By the end of the book, you will understand the latest business and IT trends in digital transformation and learn the best practices to adopt for business assurance.
Table of Contents (56 chapters)
Testing Practitioner Handbook
Credits
About the Author
Acknowledgement
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

How is testing done differently for big data/Hadoop applications?


How has testing changed with evolution of big data (as compared to testing enterprise data warehouse)?

One of the angle to it is the increasing use of big data over cloud, resulting in increased convergence of Analytics and Cloud.

I would like to offer a viewpoint based on three criteria – data, platform, and infrastructure and validation tools:

  • Software and data: Big data applications work with unstructured/semi-structured data (dynamic schema) and compared to static schema with which EDW applications function. Hence, while EDW applications can do with testing based on sampling, big data applications need to test the population. Testing the data for volume, variety, and velocity here means testing for semantics, visualization, and real-time availability of data, respectively.

  • Platform: Since big data applications are hosted on cloud (platform as a service), the applications need to be tested for ability of distributed processing...