The Insider's Guide to Next-Generation BPM
By Stephanie Best, Director of Product Marketing, Syncsort Inc. , 10/01/2010
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It is widely accepted that more than 70 percent of the world's transactional production applications are running on mainframe platforms, with a typical enterprise having millions of lines of legacy code in production. Even so, technology and workforce trends all lean towards open systems.
Efforts to modernize mainframe applications are often complex and include many phases, each of which has unique organizational and technical challenges. Migrating the data between the platforms is one of the earliest phases and a critical prerequisite to all other phases. Although it is seemingly the simplest phase, it is filled with hidden complexity and is frequently a point of failure. Success rates have been estimated to be below 20 percent.
These metrics are not surprising for several reasons. First, most mainframe applications have been accumulating data for years, even decades, resulting in volumes of legacy data that can exceed hundreds of terabytes. That's a lot of information to handle. Additionally, mainframe data sources and formats are notoriously difficult to interpret, manipulate and convert. Also there is the daunting requirement to perform equivalent data integration and transformation tasks, such as sorting, merging, copying, and joining in the open systems environment, while maintaining high levels of performance, scalability and reliability. This must take place without requiring significant or complex development efforts.
Organizations must plan for and properly address these data considerations in order to successfully accomplish their mainframe application modernization initiatives.
Requirements for Data Migration and Integration
A critical - and challenging - part of a mainframe modernization initiative
is converting the massive amounts of mainframe EBCDIC data into ASCII format
while preserving packed-decimal and binary numeric values. Since the volume
of data to be migrated can be huge, it is critical to be able to process large
data sets without failing. Ideally, data needs to be processed both quickly
(short elapsed time) and efficiently with a minimum use of hardware resources.
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