RDt Solution

Big Data Testing


The prerequisite for value creation from big data is to have proper governance processes in place to ensure the quality of data. Comprehensive analysis and research of quality standards and quality assessment methods for big data are essential for the success of Big Data programs. Big data relates to data creation, storage, retrieval and analysis that is remarkable in terms of volume, variety, veracity and velocity.

Why Is Big Data Migration Testing Important?
Integrity, validity, completeness and accuracy of data are extremely vital for organizational growth and attaining overall business objectives. Big Data is increasingly getting popular today, as it empowers enterprises and top management professionals to make informed decisions leveraging a variety of structured and un-structured data assets based on historical as well as contemporary data points.
Big data testing is crucial for business enterprises. Since this significant amount of data must be processed quickly, there is a risk of errors occurring. Data issues can be hard to find yet have a negative impact on business. Big data migration testing identifies these issues early, so you can find and apply a solution right away. This testing prevents wasted resources and makes the transition simpler.
Testing is also crucial because it verifies that the target system or software has not degraded the system performance. Testing the upstream business-critical applications that feed into Big Data systems helps you avoid duplicity and redundancy with the data sources.
RightData™'s Hadoop-powered scalable processing engine provides comprehensive capabilities for Big Data testing. RightData™'s scalable data testing engine (RDt) allows the user to easily create test scenarios between disparate systems, while automating the whole testing process to ensure data is ingested into the Big Data platform while maintaining its reliability and trustworthy-ness. Big Data Testing can be broadly divided into three steps.
Data Staging Validation
The first phase of big data testing — also referred to as pre-Hadoop stage — involves process validation. During this phase validation includes:
  • Validating data from various sources, like relational database management systems (RDBMSs), weblogs, social media, etc., to make sure that correct data is pulled into the target system.
  • Comparing source data with the data pushed into the Hadoop system to make sure they match.
  • Verifying the right data is extracted and loaded into the correct destination.
Map Reduce Validation
The second phase is a validation of "MapReduce." In this stage, the tester verifies the business logic validation on every node and then validates them after running against multiple nodes, ensuring that:
  • The MapReduce process works correctly.
  • Data aggregation or segregation rules are implemented on the data.
  • Key value pairs are generated.
  • The data is validated after the MapReduce process completes.
Output Validation
The final or third phase of Big Data testing is the output validation process. The output data files are generated and ready to be moved to Cloud storage or any other destination based on the requirement.
Activities in the third stage include checking that:
  • The transformation rules are correctly applied.
  • The data integrity and successful data load into the target system.
  • There is no data corruption by comparing the target data with the HDFS file system data.
Get Your Big Data Migration Tools From RightData
Since 2016, RightData has been committed to helping our customers with data testing and pipeline building for migration. We'll help you meet your data analytics goals with our branded product solutions. As a result, we'll make your data work for you.
RightData has the big data migration tools your company needs to verify data quality.
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