Big Data Testing Perspectives for Emerging Businesses
Everything new businesses need to know about big data testing.
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In this era of technological transformation, Big Data and analytics possess tremendous significance for enterprises, as they have become game-changers 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.
To address challenges like cost reduction, customer experience, and creating more efficient processes, enterprises must make effective implementation of Big Data in their projects. A report by BCG states that about 58% of CMOs believe that SEO, E-Mail, and mobile communications are the areas where Big Data systems are having the largest impact on their organizations.
You may also like: Big Data Testing: How to Overcome Quality Challenges.
What Is Big Data?
Big Data revolves around the five 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 and requires categorization, analysis, and visualization.
- 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 the following methodologies to process Big Data:
- Testing Strategy: Devise better test strategies to automate the process that will collect valid data in a required (structured) format to analyze and understand in a manner that is in line with business objectives
- Functional Testing: Functional testing is needed across the five Vs of Big Data – Velocity, Volume, Variety, Veracity, and Value — in order to validate and verify the outcomes at each stage and to eliminate defects and meet customer expectations and requirements
- Performance Testing: Big Data testing involves processing large amounts data in a short period of 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
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 data across different systems and servers. Hadoop helps process data on one of the replicated servers in case one server is down.
- MapReduce: Optimize and handle a gigantic quantity of structured, unstructured or semi-structured data.
- PIG: Used to analyze 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 a 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, especially in the long run through:
- Advanced testing strategy for decision making through readily available quality data.
- Business forecasting using structured and unstructured data.
- Improved cost-effectiveness on storage.
- Enhanced client expectations on different large data sets.
- Instant error identification.
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