Case Study

Cable television giant accelerates and de-risks workload transformation using automation

Enhances productivity by automating the transformation of complex Netezza procedures to a cloud-compatible big data platform


Challenge

A leading US-based cable television company was looking to reduce their data storage and processing costs by transforming Netezza workloads to a cloud-compatible big data platform.

They wanted to:

  • Get an advanced blueprint of the target architecture through a data-driven workload assessment
  • Convert Netezza procedures into Hive compatible scripts
  • Migrate relevant schema and datasets
  • Verify transformed procedures
  • Ensure data integrity and quality
  • Leverage their existing investments in line with business goals
  • Ensure business continuity and risk-free migration

 

88% automated logic transformation of Netezza procedures and schema into Hive compatible scripts

 

Solution

The Impetus Workload Transformation Solution automatically transformed the client’s data and procedures from Netezza to equivalent target compatible scripts on a cloud-compatible big data platform. Leveraging a repeatable, verifiable methodology, the solution automated schema and data migration, data migration quality checks, SQL scripts and procedures transformation, and code optimization to comply with their production SLAs.

Some highlights:

  • Provided schema optimization recommendations for partitioning, clustering, and number of buckets based on the dataset
  • Migrated the existing dataset onto Hive
  • Achieved 88% automated transformation of Netezza procedures into Hive compatible scripts
  • Executed transformed procedures in the Hive environment
  • Auto-validated the transformed procedures against Netezza results

 

50% cost and effort savings compared to manual migration

 

Impact

The Impetus Workload Solution enabled the cable television company to enhance programmer and quality analyst productivity by automatically transforming 88% of queries into equivalent HiveQL. It helped the client achieve:

  • Risk-free migration with zero business disruption
  • 50% cost and effort savings compared to manual migration
  • Reuse of existing investments
  • Faster time-to-market
  • Informed data-driven decision making
  • Data integrity through validation and quality checks
  • Optimized performance in the target environment

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