Challenges of Data migration Testing

1. Challenges Of Data Migration Testing

2. It is necessary to remark that testing a data migration must start properly in advance of the actual data being migrated. It is very typical that one of the essential commodities that a business control, owns, or controls is its data and hence any data migration must be considered high risk and should be implemented to significant verification and validation efforts. A business may decide to reduce the risk level against the data migration, but it is always reasonable to start off with data migration as a high-priority task to make sure it is successful. Some important trends and solution patterns that must be analysed while creating efficient data migration testingare agile, big data, DevOps, cloud, service virtualization with TDM, accelerators and domain-specific solutions, and automation to reduce time-to-market and increase efficiency. This document gives some of the keys consideration points to a test team so that effective test planning and strategizing can be achieved for an ETL project.

3. The Challenges of Testing with Production Data • Currently, most of the migration testing is performed with all or a subset of data from the actual production legacy database. Utilizing production data to test a legacy migration begins with all kinds of challenges. Three core challenges managing production data are the following: • Production data is subjected to how the data was entered by users in the production environment and thus does not lend itself to clear patterns of data that can be simply quantified and analyzed. • With the exclusion of automated processes that act upon production data to build new production data (e.g. annual payment schedule, weekly calendar, etc.), production data is by nature relational in structure but random in the pattern except enough user data is entered consistently to build a pattern. • Production data includes sensitive data that should be masked and pruned before the data can be used in the testing environment; but, the pruning of sensitive data could change the outcome of algo. That act upon the data because the value of the data has been changed. • Production data may need quite a bit of query analysis in order to determine subsets of data that include a consistent enough set of data to verify a pattern of data that can be used to test and validate the migration process.

4. Big Data Implementation: Intricate problems with any Big Data Migration include data quality, policy adherence, governance, and validation just to name a few. QualiDI addresses these problems and more through its different, full-service design. QualiDI is a programming-less, test-automation tool that performs execution of Big Data for data processing a success.

5. Post-Data Migration Testing • Data migration testing is by far the very significant part of the migration testing. In a condition where we do not have sufficient time allocated for testing, we can immediately jump into this phase of testing. The testing is divided into two parts: • Record Counts • Data Mapping • Unmapped Record Counts • Unmapped Record Values

6. Testing Approach: • Big data is still rising and their many onuses on testers to identify innovative approaches to test the implementation. Testers can build small utility tools using excel macros for data connection which can help in deriving a dynamic structure from the different data sources during the pre Hadoop processing stage. • Test data architects in general, concentrate more on test data services such as data masking, data creation, data sub setting, data provisioning, and more, along with tools and procedure aids to help the creation of the enterprise test data plan. • A general term for exercising a data migration responsibility is to perform an ‘Extract, Transform and Load’ (ETL) function which detects extracted data from different sources being corrected and then loaded into the target data warehouse.