Data is the bread and lifeline of any organization. Data is growing bigger day by day. Finding the required data that is essential for business needs from the huge volume of data is the greatest challenge for any business.
Some of the recent challenges faced with Big data include data volume, data variety, and the data sources.
The only way this can be overcome is through a quality analysis of the data. Testing this large volume of data is QA`s need of the hour. Selecting the apt QA service partner also brings in an added advantage to your firm`s Big data testing needs.
Read more about: Top 10 Big Data Companies in USA
WHAT IS BIG DATA & WHY DO WE NEED TO TEST IT?
Big Data is a collection of datasets that are complex, large, and difficult to process. They are unstructured, structured, or semi-structured in form.
Big Data Testing is the need for the hour in the Software Industry. Effectively managing, maintaining and using this data for Testing is a challenge. Many organizations wish to derive value from this data to enhance their business operations. The first step for this is to test the data which makes it challenging due to its complexity.
For the Big data testing strategy to be efficient, the “4Vs” of big data must be regularly monitored and validated.
The 4v`s of Big Data are:
- Volume (Scale of the data)
- Velocity (Different forms of data)
- Veracity (Certainty of data)
- Variety (Analysis of streaming the data)
KEY ASPECTS OF BIG DATA TESTING
To conduct a successful test for analysing the quality of the Big Data, the QA team needs to ensure an error-free processing of the data. Some of the key aspects of the Big Data Testing include:
Functional testing: This testing is performed to identify the data issues due to coding errors or errors in node configuration.It is performed in three phases;
- Validation of pre-Hadoop processing
- Validation of MapReduce process
- Validation of ETL process
Non-functional testing: it is performed to clear the bottlenecks and to validate the non-functional requirements. Non-functional testing is conducted in two phases.
- Performance testing
- Failover testing
CHALLENGES IN BIG DATA TESTING & WAYS TO OVERCOME THEM
75% of businesses are wasting 14% of revenue due to poor data quality. Testers can process clean and structured data efficiently, but there has always been the need to test unstructured and semi-structured data. Some of the key issues that need to be focused on are- Data security, performance issues due to heightened data, and scalability of data storage.
Listed below are the 10 most commonly faced Big Data testing challenges and how they can be overcome.
- High amounts of down-time: Many issues are caused due to the failure of checking during data collection processes. This Big data can be tested instantly to avoid the reduce in total down-time.
- Scalability issues: Issues with scalability arise when the volume of data gradually increases in the development cycle. The final performance of the application might not be as good as the initial run due to the increase in volume of the data. This can be overcome by utilising smart data samples that can test the framework of the development cycle application during key moments.
- Poor efficiency: Information from datasets across various channels are extracted for the purpose of performing data analysis. This data is extremely complex and is highly prone to be inaccurate. This can be overcome by testing the quality of the data sourced which can also enhance the reliability and overall efficiency of the process.
- Bad optimization: Big data and predictive analytics are useful in creating new business processes. The inability to handle those data over a long time can result in improper optimisation of the processes thereby disrupting the results. This can be overcome by adopting a right testing mode.
- Inadequate Quality: The characteristics of the big data need to be checked during the ETL process and storage. To avoid issues with lack in quality of bid data, an organisation must invest in checking the data for functionality.
- Security issues: Securing the big data is a challenge that is faced by many organisations. Datasets that have confidential information are to be kept safe. Hack-proof data and fraud minimisation can be achieved by implementing various layers of testing the application/software using different testing methods such as security scanning, vulnerability testing, penetration testing, and so on.
- Performance issues: Big data applications interact with live data for real-time analytics. For this to happen smoothly, the performance must be tested. Performance testing along with tests for scalability and integration can overcome the challenge of performance issues.
- Digitalization: Even in the digital era, some of the documents are maintained in a non-digital form. These need to be converted into digital forms for safety and user interface purposes. This conversion might be a challenge at times. This can be overcome by adequate testing of the data before conversion to avoid corrupted files and loss of data into the digital form.
- Functionality issues: The access to various datasets is the greatest advantage of Big data. When the results of analysing this big data turn out to be inconsistent, it can backfire the progress of the organisation. This can be overcome by testing the variability and accuracy of the datasets using appropriate testing methods.
- Competitive advantage: For an organisation to stay competing with the existing brands in the market, it needs to focus on the product/service differentiation and competitive advantage. Analysis of the Big data of the organisation can help in figuring competitive advantage strategies that can enhance the business. This can be achieved by using the right tools for testing.
Big Data being the buzzword recently, can get its place only when it is tested regularly. Continuous and adequate audit of data, classification of data and analysis can help organisations reach heights.
Ensuring the good quality of datasets can ensure business growth. Conducting regular functional, performance and non-functional tests on the existing big data can reduce the risks of data mishap and data security.
Identifying the issues with the big data needs to be done at an early stage to minimise the risks pertaining to Big data, thereby ensuring safety, efficiency, and reduced costs.
Vaibhavi is a Digital Marketing Executive at Indium Software, India with an MBA in Marketing and Human Resources. She is passionate about writing blogs on the latest trends in software technology. Her passion further encompasses writing blogs on fashion, religious views, and food. Singing, dancing & mandala artwork are her stress busters. Sticking to the point and being realistic is her mantra!