In this era of technology transformation, Big Data and Analytics possess tremendous business significance for enterprises, and that it has become a big game-changes in most, if not all types of modern industries in the last few years. As Big Data continues to influence our day-to-day lives, finding real value in its use is imperative. It helps enterprises drive their various marketing and sales campaigns and make informed decisions to obtain the desired outcome.
To address the challenges like cost reduction, customer experience and making the existing processes more efficient, enterprises must make effective implementation of Big Data in their projects. A report by BCG states that about 58% CMOs believe that SEO, E-Mail and Mobile communications are the areas where Big Data systems are having the largest impact on their organizations.
What is Big Data?
Big Data revolves around the 5 Vs (stated below) of data that are generated in various forms at various times and places which enable enhanced insights for decision making and process automation:
- Velocity: Data generation rate is profound as test data is being designed, authored, executed, logged, and processed too frequently
- Volume: Approximately 2.5 quintillion bytes of data gets created daily
- Variety: Data generated through diverse types of test data for varied testing types such as functional testing, performance testing, security testing, etc.
- Veracity: Test data generated through various sources can be structured or unstructured data and needs categorizing, analysis and visualization to make
- Value: Value can be derived from Big Data, which is possible only when the data is structured and streamlined
How is Big Data Testing Beneficial for Enterprises?
Big Data focuses mainly on achieving quality data in order to make better decisions and help improve top line and bottom line. With a major amount of testing on data validation, the actual system testing sometimes takes a backseat. To overcome this issue and to gain the maximum benefits of Big Data, enterprises must adopt some methodologies to process Big Data:
- Testing Strategy: Devise better test strategy to automate the process that will collect valid data in required (structured) format to analyse and understand and is in line with the ultimate business objectives
- Functional Testing: Functional testing is needed across 5 Vs of Big Data – Velocity, Volume, Variety, Veracity and Value in order to validate and verify the outcomes at each stage to eliminate defects and meet customer expectations/requirements
- Performance Testing: Big Data testing involves processing large data in short time, hence performance needs to be validated to gauge the Speed, Scalability, Stability under variety of data – Structured, Unstructured and semi-structured; testing mixed conditions and monitoring time consumed under varying data to find defects and delete the blockers affecting performance
What are the tools to test Big Data?
The following tools help accomplish proper Big Data testing and realize quality data in order to make better decisions:
- HDFS (Hadoop Distributed File System): Replicates the data across different systems/ servers. Helps process data on one of the replicated servers in case one server is down
- MapReduce: Optimize and handle gigantic quantity of structured, unstructured or semi-structured data
- PIG: Used to analyse larger sets of data, representing them as data flows. It is generally used with Hadoop and helps perform all the data manipulation operations in Hadoop
- Ambari: Provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs and makes Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters
Benefits of Big Data Testing
Big Data Testing helps enhance 360° view of testing services, client satisfaction, investment and profit by taking all meaningful information about the project with insights to drive high “Value” and maintain long-term relationship. It will ultimately increase efficiency and revenue for the organization even in the longer run through:
- Advanced testing strategy for decision making through readily available quality data
- Business forecasting using structure and unstructured data
- Improved cost effectiveness on storage
- Enhanced client expectations on different large data sets
- Instant error identification
Get more insights on how our testing solutions is helping leading enterprises in their digital transformation journey by vising our website: Software Quality Engineering Services