Datras Dynamic ABAP (White Paper): Export of Industrial Amount of Data from SAP
Datras EPTL is an integrated solution which covers the complete process of data quality management (DQM), i.e. data analysis, data transformation and data integration of all sorts of company data. The basis for all activities are business rules that are pre-built or user-defined, exchangeable and reusable. The integrated repository includes pre-built industry-specific rules and resources. Therefore user-specific DQM processes can be started directly with just few adjustments.
Extract & Profile
Profiling result are linked to the source data and can thereby used as filters for subsequent data quality activities like transform, load etc.
Expressions: Set of regular expressions as basic components for the
Params: PParameters for dynamic control of sequences, in particular the setting of time frames for Extract and Profile steps
Masks: Masks for controlled transforming of data types and data contents
Rules: Predefined and industry-specific business rules to create and
customize user-defined profiles
Compares: Predefined impreciseness profiles for detection of duplicates
Ressourcen: Complete sequences from Extract to Load with all required components
Profiling result are linked to the source data and can thereby used as filters
for subsequent data quality activities like transform, load etc.
Transform & Load
Based on the profiling results data types as well as data content can be manipulated (data transformation and data cleansing) using so called Converts. These Converts can be controlled using Masks which describe the desired transformation. In case of defective data supplemen-tary information is added to the data records to help users identify and correct data anomalies.
The transformed data can be filtered and distri-buted according to the profiling results and the transformation errors into different and independent SQL databases for subsequent processing.
Powerful and transparent doublet analysis which allows users to define the degree of impreciseness individually. In addition, the analysis can be extended to identify doublets based on information chains.
Compares allow the user to define the degree of impreciseness search individually and transparently and to save such impreciseness definitions in the repository for further analysis.
Datras EPTL provides a client-server architecture to allow users to develop and maintain data rules decentralized and to operate complete data quality activities centralized (on one or more servers) using “Tasks”.
This allows organisations to completely automate all their data quality activities on central server systems for production processing in an unattended operation manner.
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