The software testing community has come through many interesting trends through the year 2017. These trends mainly include the newly introduced practi...Read More
Big data is a collected data of the –web data, E-commerce, online bank transaction(credit card, debit card, wallets), social network sites.
a) Like Relational Data (Tables/Transaction/Legacy Data).
b) Text Data (Web).
c) A mobile company (like: Nokia) collects and analyzes the huge amount of data.
Several companies do the depth analysis for their big data. Many times they fail to achieve desired objective out of it due to faulty data structure, complex algorithms.
Big Data testing can be beneficial in many ways.
1. Reduces Downtime: There are many applications run on the data. In case of bad data the effectiveness and the performance of the application can be affected. Deployment of Big Data applications revolving around predictive analytics, organizations might face throng. Therefore, testing needs to be done comprehensively to avoid glitches during deployment. It helps to improve data quality and related processes of the application which further reduces the overall downtime.
2. Scale Data Sets: In the beginning of any application development, it starts from the small data sets and gradually shifts to the larger ones. Applications based on smaller data sets work good. But what if the results get changed with the different data sets? Then there is chance of failure of application. To avoid such problems enterprises adding testing process as an integral part of their application lifecycle to ensure that the performance do not get affected by small or big changes in data sets.
3. Lessens Threat to Data Quality: Every organization want that data should be valid, precise, consistent and unique. If it lacks any of above points then there are some chances to threat of quality of data only if the rigorous testing of data can save data from becoming degradable and redundant.
4. Ensures Reliability and Effectiveness: The process of collecting data from different data sources can get chances of inaccurate and unreliable data. Faulty and inaccurate data increases the risk of failure if the applications are running with real time data. Big data testing checks the data from its root to end. Which includes verification of data layers, components, and logics. This helps ensure reliability and effectiveness of data.
5. Validates Real-time Data: Big data applications uses the live data, there are need of some filtering, sorting and analysis to ensure that the captured data is valid and useful. For these scenarios performance testing of the data ensure that the application processes accurate data in real-time.
6. Provides Data Security and Authenticity: Security and authenticity is the extreme importance for the enterprises those deal with client application and host their data on their server. To maintain the security and confidentiality they have to perform big data testing at different levels to avoid security breach.
7. Issues with the digitization of information: Every enterprise has data or documents in paper format. As they need to convert those to digital forms, it is important to adequately test the data to ensure information isn’t lost or corrupted.
With adequate testing, enterprises can avoid the threat of data getting lost or corrupted.
8. Optimize Processes: Big Data and predictive analytics are the backbone of several process. Performing big data testing can be of great help to ensure all the data used by these processes are clean, accurate which helps to avoid loopholes.
9. Improves Return on Investment: Enterprises need to be competitive in the Big Data and Predictive Analytics strategy. Adding testing as a mandatory activity before any analysis and processing will ensure that the enterprises use the accurate data which helps obtain the best output.
10. Ensures Consistency: Enterprises use a variety of applications that uses with different data sets which can be cause of data inconsistencies. The results acquired over time with Big Data applications and predictive analytics to be inconsistent, it becomes a case of hit or miss for the organization. Testing allows them to determine variability accurately and eliminates uncertainty.
All the above points show that the Big Data testing is important for enterprises to take corrective decisions and process right information for performing necessary functions.