How is Big Data Testing enhancing value for Digital Enterprises

How is Big Data Testing enhancing value for Digital Enterprises?

Forrester Research has stated that, 44-percent of enterprises use data analytics and mining to boost consumer response rates and generate insights that guide executives in developing relationship-driven strategies. Even a report released by Boston Consulting Group (BCG) states that 58-percent of chief marketing officers (CMOs) believe that search engine optimization (SEO), as well as email and mobile communications, are the areas where big data systems are having the largest impact on their organizations.

Big Data and Analytics brings tremendous business significance for enterprises, which makes Big Data Testing absolutely critical. It helps enterprises to drive their various marketing and Sales campaigns, and take informed decisions for deriving the desired outcome.

Understanding Big Data

However, before we get into the testing services using big data methodology it is very important to understand various aspects about Big Data. Big Data is high-Volume, High-Velocity, and/or High-Variety information assets that are being generated in various forms. It enables enhanced insights for decision making and process automation.

Here is the ‘V’ method to further understand Big Data. It helps to understand the Volume, Velocity, Variety, Veracity and Value of data.

  • Velocity – Every minute, hours of test data is being designed, authored, executed, logged, and processed. The rate at which data gets generated is unfathomable.
  • Volume – Everyday almost 2 quintillion bytes test data that equals to 40000000GB data gets generated.
  • Variety – Data gets generated even through different types of test data such as performance testing, functional testing, security testing, or any kind of testing.
  • Veracity – The test data generated via various sources can be structured and unstructured. This data needs categorizing, analysis and visualization to make it relevant and usable.
  • Value – Value can be derived from Big Data, which can happen only when the data is structured and streamlined.

Relevance of Big Data Testing for enterprises

With Big Data Testing, major amount of testing effort is spent on data validation rather than testing the system. To overcome this and minimize the efforts there are different ways to process big data.

  • Testing Strategy: Draw better test strategy that automates the process to collect valid data in required format (structured format) to analyze and understand. The test automation strategy must be in line with the ultimate business objectives.
  • Functional Testing: Functional testing is needed across every facet of Big Data – Volume, Velocity, Variety, and Veracity to validate the outcomes. It is very important to perform and verify at each stage to eliminate defects and meet customer expectations/requirements.
  • Performance Testing: Test the performance to gauge the Speed, Scalability, Stability under variety of data – Structured, Unstructured and semi-structured. It involves processing large data in short time. Testing mixed data conditions and monitor the time consumed under varying data. The goal is to find the defects and delete the blockers that might affect the performance.
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Challenges & Tools

There are few challenges that testers may face during testing real time data. It might be due to unavailability of tools, monitoring and diagnostics, etc. To overcome this there are few tools, which makes tester’s life easier to overcome challenges.

  • HDFC (Hadoop Distributed File System) helps to replicate the data across different computers/servers in case one server is in down, the data will be processed on one of the replicated server.
  • MapReduce optimize to handle massive quantity of data which could be structure, unstructured or semi structure.
  • Similarly, PIG tool is a high-level language that generates code to analyze large data sets.
  • Ambari tool manages and monitor Hadoop cluster through an initiative web UI.

Benefits of Big data Testing

  1. Implementing new strategy
  2. Improves cost effectiveness on storage.
  3. Improves client expectations on different large data sets
  4. Helps in forecasting business by structure and unstructured data
  5. Helps in identification errors instantly
  6. Data ready available for decision making and Reduction in time

Big Data Testing helps to enhance 360 degree 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.

Testing Big Data applications requires a specific mindset, skillset and deep understanding of the technologies, and pragmatic approaches to data science. Big Data from a tester’s perspective is an interesting aspect. Understanding the evolution of Big Data, What is Big Data meant for and Why Test Big Data Applications is fundamentally important.

Cigniti leverages its experience of having tested large scale data warehousing and business intelligence applications to offer a host of Big Data Testing services and solutions. Cigniti Testlets offer point solutions for all the problems that a new age Big Data Application would have to be go through before being certified with QA levels that match industry standards.

Connect with us to derive enhanced results from your Big Data initiatives and even testing efforts.

Mukul Patiar

Mukul Patiar has been associated with Cigniti Technologies Ltd as a Senior Quality Assurance Engineer, with overall experience of more than 8 years. He is responsible for QMS definition, implementation, and maintenance. He has also worked upon process enablement for various groups and functions within the organization, including the Information Security Management System (ISMS)/ISO 27001 and CMMI-SVC. Mukul is also an automobile enthusiast and loves to ride across the city.