Testing Big Data
Testing Big Data For Quality Assurance
Testing Big Data is becoming one of the most crucial aspects of Quality Assurance, even more than web applications, user interface or any other type of digital interfaces. Big Data does affect organizations’ financial success, but more crucially, it impacts their business integrity. But, how does an organization go about testing Big Data? Do organizations use resources to check each line item? What about the challenges of the testing?
How Do Organizations Test Big Data?
Organizations must consider the efficiency, the road map and the most logical sense in testing Big Data. For example, Big Data could result in hundreds of thousands of records, each unique. But testing can’t cover all those records in an expedited time. We, here at Awesomeqa, are going to share some of our approaches to Big Data Testing that have brought successes to our clients in addition to adding an impact to the services we provide our clients.
In the most efficient and effective manner, using fundamentals from Six Sigma, best approaches to testing, and awesome test engineers, Big Data Testing can be broken down into three steps.
- Pre-Hadoop Stage: This is the data process validation step. Engineers will ensure that the process is succinct, intact and correct.
- Map Reduce Validation Stage: This is the crucial step as the business logic is tested and validated on every node to ensure that nothing is amiss.
- Output Validation Stage: The final step is to validate the data files are correct and ready to be moved to any Data server or other applicable source.
Organizations can get nervous hearing that there are only three steps to Big Data Testing because the number of records may alarm them. But in reality, using these three steps with an experienced test engineer is going to be the biggest bang for the buck for the organization.
What Are The Challenges In Testing Big Data?
Every testing assignment poses challenges, Big Data related or not. The other side to Big Data testing is the performance and stress testing. A strong performance test engineer is required for this step. But, like every assignment, stretching deadlines and cost are really the top two challenges followed by performance and stress. As deadlines get pushed back farther, the data pool can become larger than originally planned and scoped for. With the deadlines stretching out, an increase in costs is no exception. Both factors will cause some modifications and alerts to be made to the project plan and scope.
These four challenges are easy to overcome and not as detrimental to the organization as it sounds. With the right test engineer, Big Data testing is the latest approach to testing in organizations today. Everything is about protecting the organization’s data!