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Batch Offloading

Overview

Most mainframe workloads consist of expensive batch processes classifiable into one or more of these categories :

  • End of day/month/year processes
  • Periodic batch/transactional processing
  • Report and statement generation
  • Data ingestion and extraction into mainframe database (DB2, IMS, VSAM)
  • Data transformation and transmission
  • Data archival and purge

Infosys helps clients identify and offload workloads on to the most suitable platform, deliver better performance, and reduce costs significantly, with leaner and lighter mainframe.

  • Low value, poorly performing jobs are best suited for Hadoop platform
  • Periodic, mission critical jobs are ideal for Spring Batch
  • Batch processes that are typically involved in Extract, Transform and Load are ideal for ETL platform
  • MongoDB suits giant databases

Challenges & Solutions

Spring Batch’s inherent architecture and design are loosely coupled and amenable to automating of unit and system testing.
It also integrates easily with DevOps toolset.

Our seasoned, knowledgeable experts along with a plethora of accelerators and tools, and complete understanding of mainframe applications, are key in defining the roadmap to a non-disruptive offloading journey

ETL can match the same (if not higher) level of performance and throughput by:

  • Reducing I/O
  • Limiting database reads/writes
  • Partitioning and parallelizing
  • Bulk load/eliminate logging
  • Filtering as soon as possible

Infosys is heavily invested in modernizing mainframes, and believes organizations faced with a declining skill set due to retiring mainframe experts need to leverage tools to generate business/functional documents, and increase the productivity of the remaining