Enterprise data has grown 650% in the last five years, as a result about 85% of Fortune 500 organizations will be unable to exploit Big Data for competitive advantage. Data is the lifeline of an organization and is becoming more important each day. Big Data Testing is a trending topic in the Software Industry, its various properties like volume, velocity, variety, variability, value, complexity and performance creates many challenges. On click of a button we generate megabytes of data. Structured business data is supplemented with unstructured data, and semi-structured data from social media and other third parties. Big Data is a collection of data sets which are large and complex and are difficult to process and does not fit well into tables and that responds poorly to manipulation. Finding essential data in such a large volume of random data is becoming a real challenge for businesses, and quality analysis is the only option. So, a strong set of test data and robust QA environment are required to ensure error-free processing of data.
Actual understanding of the data and its utilization for benefit in the business is a real challenge. Also, dealing with unstructured data drawn from sources such as tweets, text documents and social media posts is one of the increasingly challenging for QA teams.
There are various business advantages of Big Data mining, but separation of required data from junk is not easy. The to achieve this the QA team has to overcome various challenges such as:
Volume and high label of Heterogeneity in data set
Today, businesses have to store Petabytes or Exabytes of data, mined from numerous online and offline sources, to run their day to day operation. Testing a huge volume of data is a challenge in itself. Testers are required to review such huge datasets to confirm that they are a fit for decision making in commercial purposes. How can you store and prepare test cases for such large datasets if the data is not consistent and full-volume testing is impossible due to size?
Acceptance of the Data in system
For a Big Data testing strategy to be effective, testers need to continuously monitor and validate the 4V’s of Big Data – Volume, Variety, Velocity and Value. Acceptance of the data for the business is the real challenge faced by any Big Data tester. It is not easy to measure the testing efforts and strategy without proper knowledge of the nature of available data. Testers need to cognize commercial rules and the connection between different subsets of data. They also have to appreciate the statistical relationship between data sets like, correlation between different data sets for proper benefits analysis.
Emotions in Data
All the unstructured data generally extracted from sources such as tweets, text documents and social media posts supplement primary data feeds. Dealing with emotions is one the biggest challenge in such unstructured data. For example, consumers tweet and discuss about a new product launched in the market. Testers need to capture their emotion quotient and convert this into insights for decision making in business situations.
Stretched Deadlines & Costs
If the testing process is not standardized and strengthened for re-utilization and optimization of test case sets, the test cycle / test suite would go beyond agreed parameters and result in increased costs, maintenance issues and delivery slippages. Test cycles might stretch into weeks or even longer in manual testing. Hence, test cycles need to be accelerated with the adoption of validation tools, proper infrastructure and data processing methodologies.
Variation in big data technologies
It can be easy for a tester to get lost in the variety of big data technologies and platforms available on the market. Do you need Spark or would the speeds of Hadoop MapReduce be enough? Is it better to store data in Cassandra or HBase? Finding the answers can be tricky. And it’s easy to make a bad choice if you are exploring available technological opportunities without a clear view of what you need.
Technical Expertise and Coordination
Technology is growing, and everyone is struggling to develop new algorithms to process Big Data efficiently. Testers understand that they have to think beyond the regular parameters of automated testing and manual testing. Big Data, with its unexpected format, can cause problems that automated test cases fail to understand. Creating automated test cases for such a Big Data pool requires expertise and coordination between team members. The testing team should coordinate with the development team and marketing team to understand data extraction from different resources, data filtering and pre and post processing algorithms. It calls for a remarkable mindset shift for both testing teams within organizations as well as testers. Also, organizations need to be ready to invest in Big Data-specific training programs and to develop the Big Data test automation solutions.
Security holes in Big data.
Big data technologies evolve, but their security features are often neglected, since it’s hoped that security will be granted on the application level. And as a result of technology advancement and project implementation, big data security just gets cast aside.
These are just some of the challenges that testers face while dealing with the QA of a vast data pool. The Big Data testing needs are different from regular software evaluation since it focuses less on the functionality and more on the quality of the data as if flows through the process. The most significant contribution of Big Data testing to software development will probably be linked to developing new ways to make sense of large data volumes.
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