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A data decomposition method for stepwise migration of complex legacy data
Author(s) -
Martens Andreas,
Book Matthias,
Gruhn Volker
Publication year - 2019
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2647
Subject(s) - data migration , computer science , heuristics , downtime , volume (thermodynamics) , decomposition , legacy system , key (lock) , database , computer security , operating system , physics , software , quantum mechanics , biology , ecology
Summary Sooner or later, in almost every company, the maintenance and further development of large enterprise information technology (IT) applications reaches its limit. From the point of view of cost as well as technical capability, legacy applications must eventually be replaced by new enterprise IT applications. Data migration is an inevitable part of making this switch. While different data migration strategies can be applied, incremental data migration is one of the most popular strategies, due to its low level of risk: the entire data volume is split into several data tranches, which are then migrated in individual migration steps. The key to a successful migration is the strategy for decomposing the data into suitable tranches. This paper presents a method for data decomposition where the entire data volume of a monolithic enterprise IT application is split into independent data migration tranches. Each tranche comprises the data to be migrated in one migration step, which is usually executed during the application's downtime window. Unlike other methods, which describe data migration in a highly abstract way, we propose specific heuristics for data decomposition into independent data packages (tranches). The data migration approach described here is being applied in one of the largest migration projects currently underway in the European health care sector, comprising millions of customer records.