As a greater number of organizations are adopting “Big Data” as their Data Analytics solution, defining a robust testing strategy and setting up of an optimal test environment for Big Data is becoming quite a difficult challenge. Primary reason for this is the lack of knowledge and understanding on Big Data testing as the technology is still gaining popularity in the industry. Big Data involves processing of huge volume of structured/unstructured data across different nodes using languages such as “Hive”, “Map-reduce” and “Pig”. A robust testing strategy needs to be defined well in advance in order to ensure that the functional and non-functional requirements are met and that the data conforms to acceptable quality. In this blog we intend to define recommended test approaches in order to test “Hadoop” based applications.
Big Data Testing Courses
Filled with examples and labs, this hands-on training teaches concepts and introduction HQL techniques used in Big Data testing.
Big Data and ETL Testing Fundamentals
This one week course is designed to familiarize business professionals in the Big Data and ETL space with the basics of testing and validating. This course focuses on getting professionals the knowledge required in order to successfully test and validate Big Data and ETL processes.
Introduction to Big Data Testing using Hive and HQL
This one week course of lectures and hands-on training is designed to provide students with the foundation necessary for testing data warehouses. The course covers several common transformation tests and the SQL syntax required to retrieve the data in order to perform the test.
Big Data Testing Immersion
This 2-week class combines all of RTTS’ Big Data testing courses into an intensive session designed to accelerate your learning in this area.