The Mainframe Data Access & Replication Conundrum In Today's Heterogeneous IT Environment
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Transcript of The Mainframe Data Access & Replication Conundrum In Today's Heterogeneous IT Environment
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The Mainframe Data Access& Replication Conundrum
In Today's Heterogeneous IT Environment
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Mainframe Integration without Compromise
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Agenda
• Introduction to Treehouse Software• Today’s Situation• IT Objective• tcACCESS
• Data Integration• tcACCESS SQL Engine• tcACCESS Demo
• tcVISION• Data Replication• CDC Methods• Processing Stages• tcVISION Demo
• Summary
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Introduction to Treehouse Software
• Established in 1982• Mainframe system tools vendor• Leading ISV for Software AG
• 70% penetration in North American market• Consultants with 20+ years experience• More than 300 man years of experience
• Supporting all mainframe operating systems• Focus for the past 15+ years on data migration,
replication, and integration• Over 700 customers worldwide• 20 products• 30 employees
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Treehouse Customers
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Today’s Situation
• Heterogeneous IT environments
• Legacy applications
• High-availability information systems
• Data silos
• Increasing data volumes
• Exploding costs
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IT Objective
• Intelligent data integration
• Efficient data synchronization
• Cost effective solution
Enterprise-wide Data Management through:
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Enterprise Data Integration
• Bi-directional data-exchange across heterogeneous systems
• Direct data access across heterogeneous systems
• Data transformation for data analysis and exchange
tcACCESS Concepts
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Enterprise Data Integration
• Relational access to legacy data and applications
• Data federation – heterogeneous data views
• Integration of mainframe files and DBMS structures
• Data federation between mainframe and Windows/Open Systems data
tcACCESS Concepts
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tcACCESS SQL Engine
Host/PC & Host & Web Integration
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tcACCESS SQL Engine
• More than 90 SQL functions supported
• Operators (+, -, *, /, ||)
• Conditional Operators (>, <, =, BETWEEN, LIKE)
• Logic Operators (AND, OR)
• INNER and OUTER JOINS
• GROUP BY, ORDER BY
• Security may be applied (RACF, ACF/2, Top Secret)
• Stored Procedure Support
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tcACCESS SQL Engine
• Different data sources can be JOINed:SELECT IMS.NR, IMS.NAME, VSAM.ADDRESS FROM IMS, VSAM WHERE IMS.NR = VSAM.ID
• VIEWS can be created• Control Options available
(MAXIO, MAXROW, NOORDERBY, etc)
• Global SQL Exit available• Field Level Exits available
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tcACCESS Architecture
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Demo
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CDC Replication
• Different data formats
• Different data models
• Large data volumes
• Limited batch window
• Requirement for up-to-date information
• Moving/replicating data...
• as much as needed
• as little...
• as transparent...
• as flexible...
• as secure...
• ...AS POSSIBLE
The Problem: The Solution:
...with
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Data Latency
Change Data Capture
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Data volume
Change Data Capture
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Mainframe Change Data CaptureChange Data Capture
• Efficient transfer of entire databases
• Analysis for data consistancy
• Best for Initial Load prior to log processing
• Best for periodic mass data transfer
• One step data transfer
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Mainframe Change Data CaptureChange Data Capture
• Comparison of data snapshots
• Efficient transfer of changed data since last processing
• IMS/DB, DL/I, VSAM, DB/2, ADABAS, CA-IDMS, DATACOM, sequential files
• Flexible processing options (SORT etc.)
• Automatic creation of deltas by tcVISION
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Mainframe Change Data CaptureChange Data Capture
• Usage of the DBMS logging capabilities
• IMS/DB, VSAM, DB/2, DL/I, ADABAS, IDMS, DATACOM
• Transfer of changed data in scheduled time frame
• Best for batch window
• Best for processing right after logfile creation
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Mainframe Change Data CaptureChange Data Capture
• Realtime capture of changed data
• Changes directly obtained from DBMS
• CA-IDMS, IMS/DB, VSAM, DB/2, DATACOM, ADABAS
• Secure data storage even across DBMS restart
• Flexible propagation methods
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Stage 0:Data in internal raw format
Stage 1:Data in tcVISION format (before and after images)
Stage 2:Data normalized with structure definition
Staged Processing
Stage 3: Data in DML or Loader format
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Stage 0:Data in internal raw format
Stage 1:Data in tcVISION format (before and after images)
Stage 2:Data normalized with structure definition
Stage 3: Data in DML or Loader format
Staged Processing
Exit pointsavailable at every stage
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Demo
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Summary• Relational access to legacy
data and applications
• Data Federation – heterogeneous data views
• Change Data Processing
• Bi-Directional real time replication
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SummarytcVISION Architecture
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SummaryBi-directional data-integration and data-synchronization
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E-mail: [email protected]