TestingXperts: Software & QA Testing Blog

Data holds high importance for any application. Maintaining the integrity and quality of data is vital, and hence any change with respect to data needs a formal strategy for testing. The strategy would depend on factors such as time availability, critical of data, budget, etc. and would need to be personalized according to the application.  What is Data Migration? Data migration as the name suggests is moving the data from one environment or system to another. There can be various reasons for migrating the data such as upgrading the existing version of the database, relocation of servers, upgrades in servers, etc.  This can be a cumbersome task for a complex application having large volumes of data. To craft an appropriate testing strategy , the scope of the project needs to be analyzed wisely. Data migration projects are carried out in a big bang approach as well as in an incremental fashion.  Risks and challenges associated with the data migration process

2016 was the year of Big Data; those leveraging big data are sure to rush ahead while those who do not must have fallen behind. Data evolving from mobile, purchase histories, social networks, CRM records, etc. provide companies with valuable insights to expose hidden patterns that can help organizations graph their growth story. Evidently, when we talk about data, we are discussing huge volumes that amount to petabytes, exabytes, and at times zettabytes. Together with this huge volume, this data which initiates from different sources also needs to be managed at a speed that will make it significant to the organizations. To make this organizational data useful, it has to be anticipated by the users via applications.   As with every other application, testing forms an imperative part; it is the same with Big Data applications as well. Though, testing Big Data applications has more to do with validation of the data than testing the individual features. There are a few hitch