Automated validation for data platform and workload modernization

Data estate modernization is typically a time-consuming and complex process, which requires extensive expertise and resources. Besides migrating SQL and data, enterprises must transform legacy workloads like queries, ETL, applications, reporting, and analytics to the chosen cloud environment or modern data platform. The converted code and business logic then need to be tested and validated on the target environment to ensure that all workloads perform correctly and meet the business SLAs. Any errors or issues identified during validation need to be addressed immediately, as these can lead to much-dreaded business disruption. This blog explores how automating the validation process can help businesses save time, effort, and money.

Why code validation is a Herculean task

Most migration projects go way beyond a simple ‘lift and shift.’ All legacy workloads cannot simply be moved as-is to the new environment – some need additional optimization, while others need complete re-engineering for efficient resource utilization on the cloud. The migrated applications also need to support all use cases in the new environment, for which each use case must be validated on a live dataset. However, manually validating the correctness of migrated code and applications on the target is an extremely tedious process. Here’s a glimpse of the complexities at play:

  • Disparate code types – ETL workflows, orchestrator scripts, procedural logic etc.
  • Complex business logic
  • Complex conditional logic in queries
  • Multiple enterprise scenarios and corner cases

Automated solutions that test workloads across their lifecycle and perform exhaustive quality and data-based checks can help engineering teams drastically reduce manual effort. Such solutions certify the migrated code to ensure a seamless transition into production

Ensuring validation at the minutest level

Automated tools that provide cell-by-cell validation reports can help enterprises easily debug issues and avoid errors/failures on the target platform. They perform pipeline-based validation on actual enterprise data for the following:

  • Production jobs with code, including SQL queries
  • Data, including aggregate functions such as MIN, MAX, AVG, SUM, NOT NULL, etc.
  • Processed data in the tables impacted due to job execution
  • Schema and stored procedures
  • Database views
  • ETL scripts

Automated validation can help identify and resolve any mismatches between the source input and transformed output, such as date/time format, time zone, decimal values, etc. It also compares business logic in the packaged code (like PL/SQL) with the converted code such as PySpark/cloud-native equivalent and validates the results. This guarantees consistency and parity across the migrated code and data.

Automated validation of migrated workloads in LeapLogic

Addressing security concerns

Testing the migrated data and transformed queries in the new environment is an integral part of the validation process. However, for compliance and regulatory reasons, many enterprises choose not to share their sensitive data for testing. An automated migration solution can address this challenge by generating a sample dataset with tens of unique records based on the exact query conditions and validating these with 100% accuracy. This is ideal for unit testing of the transformed queries. In addition, the solution can feed the customer-provided dataset for testing on real datasets, which is more suitable for integration testing of transformed queries.

In a nutshell

One of the major pitfalls of data estate modernization projects is the inability to efficiently validate migrated workloads within the stipulated timeframe and budget. Incorrectly loaded jobs, inaccurate logic, corner cases, or other errors can seriously impact data points and insights. LeapLogic, Impetus’ cloud accelerator, simplifies and de-risks the entire validation process, enabling enterprises to smoothly transition into production and go live on the target with confidence. It automates the end-to-end transformation process – right from assessment to operationalization, powering modernization from any legacy system (like Teradata, Netezza, Oracle etc.) to any cloud-native stack or modern data platform. To learn more, book a demo or start your free trial today.

Author
Gurvinder Arora