1. tRelational/DPS Overview

2. Agenda ADABAS Data Transfer: business needs and issues
tRelational & DPS Overview
Summary
Questions?
Demo

3. Business Needs 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

4. Business Issues 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

5. Reporting & Business Intelligence

6. Product Components
These are areas that draw upon our existing expertise in ADABAS, relational databases, performance monitoring, and development.
Mention that DPS and tRelational are a strategic focus for TSI, a significant amount of our current staff resources are dedicated to their development. TSI has been working on tRelational since 1993 and DPS (in some fashion) since 1994.
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)

7. tRelational Features Modeling and Mapping “Code” Generation
These are areas that draw upon our existing expertise in ADABAS, relational databases, performance monitoring, and development.
Mention that DPS and tRelational are a strategic focus for TSI, a significant amount of our current staff resources are dedicated to their development. TSI has been working on tRelational since 1993 and DPS (in some fashion) since 1994.
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

8. tRelational and DPS Functionality

9. tRelational and DPS Functionality
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.

14. tRelational Data Analysis and Modeling

15. tRelational and DPS Functionality
The Materialization process requires NO DIRECT ADABAS ACCESS

16. tRelational and DPS Functionality
Extracts from an
ADABAS utility backup.

17. tRelational and DPS Functionality
The extracted data is transformed
Into a relational form.

18. tRelational and DPS Functionality
RDBMS tables are then populated
by the native RDBMS loader utility
(e.g., Oracle SQL*Loader).

19. tRelational and DPS Functionality
The Propagation process requires NO DIRECT ADABAS ACCESS

20. tRelational and DPS Functionality
ADABAS transaction data is
extracted from the ADABAS
Protection Log files.

21. tRelational and DPS Functionality
The extracted data is transformed
into SQL “UPDATE”, “INSERT”,
and “DELETE” statements.

22. tRelational File Implementation
Capture logical (Predict) file,
Userviews, and physical
(FDT) definitions.

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
tRelationalPC offers an alternative GUI-based modeling and mapping environment communicating via TCP/IP with the mainframe tRelational repository.

30. tRelationalPC Auto-Generation
Auto Generation Example:
Four tables with Primary Key and
Foreign Key constraints, and the
added DPS PE Sequencer
(PE occurrence).

31. Output Generated from Metadata
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

32. Generated RDBMS DDL

33. DPS Architecture 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

34. DPS Materialization (ETL)

35. DPS Materialization 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

36. DPS Materialization Data
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.

37. DPS Materialization SQL Utility Load Control
Native loader control statements
are automatically generated with
each DPS Materialization run.

38. DPS Propagation (CDC)

39. DPS Propagation
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

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. DPS Propagation Sample SQL resulting from an update to
LANG (MU), modifying GER, ENG to
ENG, showing the Update and Delete
generated to reflect MU “compression”.

42. Summary of Benefits 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