2 ADABAS Data Transfer: business needs and issues tRelational & DPS Overview Summary Questions? Demo Agenda
3 Transfer legacy ADABAS data to integrate: Business intelligence Reporting systems Web enablement Purchased COTS/ERP application(s) One-time conversions Application reengineering/conversion Platform change Business Needs
4 Cost: development, operation and maintenance Time: deployment, execution, and maintenance Resources: human and machine Risk: data discovery/integrity and project deadlines Complexity: ADABAS to RDBMS transformations Performance: coexistence with with ADABAS OLTP Flexibility: response to discovery and change in application or requirements Vendor: product focus, experience and longevity Business Issues
6 tRelational, an ADABAS-to-RDBMS modeling, mapping, and data analysis tool Data Propagation System (DPS), an ADABAS-to- RDBMS data migration and propagation system for data distribution and warehousing tRelationalPC, a Windows-based client/server GUI data modeling and mapping environment (included with tRelational) Treehouse Remote Access (TRA), middleware that allows tRelationalPC to communicate with tRelational on the mainframe (included with tRelational) Product Components
7 Modeling and Mapping Native ADABAS/NATURAL application Predict metadata discovery and ADABAS file analysis Automated generation of normalized RDBMS schemata with explicit ADABAS field to RDBMS column mapping Robust modeling and mapping – normalize, denormalize, substring, concatenate Single rule base and metadata repository Code Generation RDBMS Data Definition Language (DDL) – create tables, columns, and constraints DPS Parameters – extract and transformation parameters tRelational Features
9 Captures Logical (PREDICT) and Physical (ADABAS FDT) file definitions and resolves any discrepancies. The implemented file provides the basis for modeling and mapping to the RDBMS table(s).
10 tRelational and DPS Functionality Captures statistical analysis to provide or confirm the understanding of the source data. The analysis provides for improved modeling and early identification of problem data.
11 tRelational and DPS Functionality Provides physical modeling and explicit ADABAS to RDBMS mapping. Auto Generation provides intelligent and automatic modeling and mapping from an Implemented File.
12 tRelational and DPS Functionality tRelational generates all input parameters needed to begin Materialization and Propagation.
13 tRelational and DPS Functionality tRelational generates output for the creation of tables, columns, and constraints for your target RDBMS.
23 tRelational File Implementation Fields that are defined logically and physically different are highlighted.
24 tRelational ADABAS File Analysis One time capture of statistical analysis of repeating data (MUs and PEs), candidate variable text data, and descriptors for improved modeling.
25 tRelational ADABAS File Analysis Statistics of MUs and PEs for sizing of child tables and potential de-normalization of tables to individual column(s).
26 tRelational ADABAS File Analysis Statistics of alphanumeric fields for candidate variable character text columns.
27 tRelational ADABAS File Analysis This screen shows descriptor/superdescriptor usage statistics to determine candidate Primary Keys and indexed columns.
28 RDBMS Schema Auto-Generation Generates table(s), columns, constraints, and mappings for a selected implemented file.
29 tRelationalPC offers an alternative GUI-based modeling and mapping environment communicating via TCP/IP with the mainframe tRelational repository. tRelationalPC
30 Auto Generation Example: Four tables with Primary Key and Foreign Key constraints, and the added DPS PE Sequencer (PE occurrence). tRelationalPC Auto-Generation
31 RDBMS Data Definition Language (DDL) DPS specifications (parameters) for ETL and CDC Processing Metadata reports (Summary and Detail) tRelational API enables Metadata export to other tools and repositories Output Generated from Metadata
33 Written in Assembler for efficiency Executed as batch job No calls to active ADABAS required No impact on production environment External Transformation Routines (ETRs) A call to a linked object Dozens of built-ins Custom transformation and data cleansing DPS Architecture
35 Provides initial load of the RDBMS Extracts from ADASAV Intelligent transformation based on model/mappings Generates rows for target table(s) and SQL Utility Load Control statements Provides refresh of the RDBMS when required or desired DPS Materialization
36 Contains all row images to be loaded into the RDBMS repository. Each row is prefixed with a Table ID, and is formatted and delimited natively for the RDBMS loader. DPS Materialization Data
37 Native loader control statements are automatically generated with each DPS Materialization run. DPS Materialization SQL Utility Load Control
39 Provides periodic synchronization of the RDBMS target with the source ADABAS database Extracts from PLOG archives Intelligent transformation based on update and model/mappings Generates SQL for Inserts, Updates, Deletes, and Commits DPS Propagation
40 DPS Propagation Sample SQL resulting from an update to Personnel ID, mapped to a Primary Key, showing the Deletes and Inserts generated to maintain referential integrity.
41 Sample SQL resulting from an update to LANG (MU), modifying GER, ENG to ENG, showing the Update and Delete generated to reflect MU compression. DPS Propagation
42 A product, not a consulting engagement Fast configuration and implementation Data analysis and quality assessment Automated schema/mapping generation and code generation Supports complex transformations out of the box Native RDBMS integration out of the box RDBMS integrity assurance Cost efficient operation Zero contention with ADABAS applications Proven, scalable, reliable, and extensible architecture Reduced risk and improved quality Seamless upgrade to real-time processing with DPSync Flexible and easy to maintain 11 Years of Treehouse focus and commitment Summary of Benefits