Big Data Testing: How to Overcome Quality Challenges

1. Big Data Testing : How to Overcome Quality Challenges

2. Content • What is Big Data Testing ? • Big Data Testing : How to Overcome Quality Challenges : • Understanding the Data • Scalability • Performance • Continuous Availability and Data Security • Future Developments • Benefits Of Big Data Testing

3. What is Big Data Testing ? • Big Data defined as a large volume of data structured or unstructured. Data may exist in any format like flat files, images, videos, etc. The primary Big data characteristics are three V’s – volume, velocity, and variety. • Big Data testing is defined as testing of Big Data applications. Big data is a collection of large datasets that cannot be processed using traditional computing techniques. Testing of these datasets involves various tools, techniques, and frameworks to process.

4. Big Data Testing : How to Overcome Quality Challenges Big Data Testing : How to Overcome Quality Challenges : • Understanding the Data • Scalability • Performance • Continuous Availability and Data Security • Future Developments

5. Understanding the Data • To introduce and implement an effective big data testing model, it is necessary for the tester to have proper knowledge relating to volume, variety, velocity, and a value relating to the data. • Understanding the data that has been captured in a huge quantity. It is paramount importance for a Big Data tester to get an idea about the business rules and the association that exists between the varying subsets of the data.

6. Scalability • Scalability is the capacity of a framework to handle bigger workloads by extending the framework in a clear way. By and by, on the other hand, it is frequently difficult to anticipate the situations that will most profit by profoundly adaptable frameworks. • Workloads can definitely extend because of business development, new application elements, and utilization examples.

7. Performance • One characteristic of Big Data is that the data is highly volatile, which is more often unstructured in format generated from various sources such as web logs, sensors embedded in devices, GPS systems etc . • What hasn’t changed through is the Business need for making best possible decisions quickly. To go through and access detailed information needed from this huge volume of data that too at a very high speed with increase in the degree of granularity makes this challenge worse.

8. Continuous Availability and Data Security • In today’s world when organization rely on data to generate revenues for data application , data should never go down. It should be always available. • Big Data contains massive amount of information, which may also contain personal ID information, account details, credit card data and some other sensitive information. Thus security of this sensitive information is needed.

9. Future Developments • Big Data testing is quite different from the usual software evaluation process that one conducts from time to time. The Big Data testing is carried out so that new ways can be found to make some kind of meaning of the huge data volumes • The processes that are involved in testing big data must be carefully selected so that the ultimate data must make sense for the tester and the organization. The future development challenges arise in the case of big data testing as it focuses on the functionality aspect.

10. Benefits Of Big Data Testing Following are the Benefits Of Big Data Testing : • Data Accuracy • Cost-effective • Early Bug Detection • Data Accuracy • Reduces Deficit and Boost Profits • Better Market Strategy

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