Get Rid of Data Complexities with Big Data Testing

September 25, 2015

Every software exhibits complexities due to thousands of non-GUI processes, complicated business logic and different databases that need to interact and intersect with each other to provide real-time information and experiences at the user’s end.

Common Data Challenges

• Heterogeneous and unstructured data spread across different layers
• Continuous explosion of data and information resulting in bad data
• Difficult business processes due to complicated business logic
• Ineffective decision making due to bad or poor data
• Increased cost of handling variety, volume and velocity of large data sets
• Performance issues due to heightened data volumes

The key to reducing these complexities lies in Big Data testing. Big Data involves large volume of structured and unstructured data warehouses stored from a variety of sources such as social media, mobile devices, intranets in languages such as ‘Hive’, ‘Map-reduce’, ‘Sqoop’ and ‘Pig’.

Big Data testing is a process of verifying and maintaining the quality, integrity and scalability of large data sets, schemas, approaches, tools and inherent processes to ensure functional and non-functional requirements of data are met accurately to perform error-free analytics.

Key Aspects of Big Data Testing

The key aspect of Big Data testing lies in providing a holistic view of data health and ensure that the data is processed without errors by validating volume, variety and velocity of data.

Volume: Tests the volume/amount of data generated within and outside your enterprise via web, mobile, cloud or other infrastructure sources exponentially each year.

Velocity: Tests the speed with which the new data is created, analyzed and processed in real-time to derive business value and digitize transactions.

Variety: Tests the different types of data across the enterprise data banks – structured, semi-structured and un-structured data, such as log files, location-based data, etc.

Value: Tests the different types of data across the enterprise data banks – structured, semi-structured and un-structured data, such as log files, location-based data, etc.

Benefits of Big Data Testing

• Helps ensure that the large data sets across multiple sources are integrated accurately to provide real-time information
• Certifies the quality of frequent data deployments to avoid incorrect decisions and subsequent actions
• Aligns the data with changing dynamics to take predictive actions
• Enables leveraging the right insight from the minutest data sources
• Ensures scalability and processing of data across different layers and touch-points

With Big Data test automation solutions, your enterprise can verify both structured and unstructured data residing at different sources and improve the quality of data warehouse which ultimately helps them enhance data quality to take insights-driven business decisions.

Want to improve your Big Data quality or increase enterprise-wide visibility with usable and authentic information to get insightful analytics? Try Big Data Testing with TestingWhiz.