BIG DATA & ANALYTIC TESTING

With the digitalization of the economy, the data is growing in burgeoning amount and the business organizations are struggling with proper data management and testing techniques. In big data & analytics testing, testing has to be majorly conducted for validating data extraction, transformation algorithm, data quality, data flow, and business rules. With the drastic development in the IT field, the amount of data generated has also become magnificent, which adversely impacts the data quality. These concerns have created the need of testing, which can maintain the data quality across big data ecosystem.

The characteristics of big data are defined by 4Vs, namely:

• Volume: In big data, the data is collected from multiple resources in huge volume. This data is growing at an exponential rate every year.
• Variety: The data variety is another challenge in big data management. As more and more information is digitized, the information variety is also increasing. The big data is characterized by structured, unstructured ad meta-data.
• Veracity: The data veracity represents the data value or trustworthiness. It evaluates the reliability of the data and if there are any discrepancies in the collected data.
• Velocity: The data velocity represents the rate at which new data is generated in digital mediums.

Test Triangle’s approach to big data testing

Test Triangle ensures complete coverage of both structured and unstructured data, which can be used to achieve superior data quality. Our comprehensive testing coverage of big data systems can help accelerate the product development time and reduce the testing cost. 

Data Integration testing

• Complete data extraction, data quality and data transformation
• Data filtering mechanism and transformation logic

Data Migration Testing

• Migration to big data lakes
• Smooth migration of Structured to NoSQL data Sources

Analytics Testing

• Testing predictive models

Visualization Testing

• Evaluating the data insights

Benefits from our services:

• Cost effective solution
• Data Security
• Testing across different platforms such as Hadoop, Oracle, Teradata and MongoDB