Safeguard data migration and transformation by minimizing risk.

IT administrators now rank data migrations among their most difficult tasks. It’s not easy to tell where companies are coming from when they migrate.

From the Old to the New System
From the software of one provider to that of another, or from local installation to the cloud.
To “migrate” data means to transfer it from one data source to another, in this case from “Legacy” to “Target.” When it comes to efficiency and savings, Data Warehousing is an essential task for any type of company. Data is extracted from customer relationship management systems (CRMs), data servers, or flat files, converted in a staging area, and loaded into a data warehouse through an ETL (Extract Transform Load) process. Information and data may be lost if it ever fails.

Various types of Data Migration Testing are

Schema Compare Tests

Verify that the schema or data model used for both the source and target systems is consistent. Users can quickly and easily query the metadata tables to extract the necessary data for validation purposes.

  • Verify that the names of the tables and columns in the source and the target are identical.
  • The source and target datatypes must have the correct mapping. In the case of an INT source column, the corresponding target system datatype should be NUMERIC.
  • Make that the indexes, primary keys, and views are consistent with one another.
Row Count Tests

The most fundamental test is to compare the record counts of the source and target tables.

One-time All of the tables’ first loads undergo row count checks.
Delta load row count verification for all or selected tables

Natural Language Processing (NLP)

Using our NLP services, machines can understand and interpret human language. We can help you automate activities, improve customer experience, and obtain deeper insights from unstructured data by utilizing techniques such as text mining, sentiment analysis, and language translation.

Data Comparison Tests

Evaluate the information in each table by comparing its rows and columns. Data migration success can then be confirmed.

If you compare the first names in the source and target columns, you will see no differences.
Verify that the date value is the same even though the two dates may be presented in different ways.

Data Aggregation Tests

When comparing the source and the target databases, companies can perform aggregated checks on very large tables. This is important because it can be prohibitively expensive to compare billions of rows in a table row by row.

When comparing the source and the target databases, companies can perform aggregated checks on very large tables. This is important because it can be prohibitively expensive to compare billions of rows in a table row by row.