Pre-Con Ed: High Performance Masking and Test Data Generation for IMS and DB2

1. World®
’16
High Performance Masking and Test
Data Generation for IMS and DB2
Prashant Pandey
CA Technologies
MFX59E
MAINFRAME AND WORKLOAD AUTOMATION

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© 2016 CA. All rights reserved. All trademarks referenced herein belong to their respective companies.
The content provided in this CA World 2016 presentation is intended for informational purposes only and does not form any type of
warranty. The information provided by a CA partner and/or CA customer has not been reviewed for accuracy by CA.
For Informational Purposes Only
Terms of this Presentation

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Abstract
With fines averaging millions of dollars, and prohibitively high
infrastructure costs, masking and subsetting the mainframe is a must.
This session will cover how the CA Test Data Manager architecture
avoids the dire security and performance implications of moving data to
and from z/OS.
Prashant
Pandey
CA Technologies
Sr Principal Consultant,
Presales

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Test Data on z/OS (the Bad News)
§ z/OS system of record (70% data still resides here)
§ Many organisations rely on copies of Production for testing
§ Secure and reliable in production – what about test?
§ What and Where is PII data
§ Legislation – GDPR, demonstrate compliance
§ Breach Notification – reputation & fines risk
§ Cost of moving test data to/from platform
(the Good News) Best Practice Test Data Management already addresses this impact

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What is Test Data Management (TDM)?
‘… is the practice of applying a structured and centralized approach to
the management of test data at an enterprise level, in order to reduce cost
whilst increasing efficiency and quality in development and testing’

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CA-Test Data Manager Deployment & Process
1. Centralize
2. Discover
3. Subset
4. Mask
5. Synthesize
6. Find & Reserve
7. Automate
Synthetic/Clone Data
Masked
Subset
Created
Production
Development
Web - SOA Test Harness
Virtualization
Data Masking
Subsetting/Data Slicing
Data Profiling
Coverage Metrics
Agile-Designer
Test Design
Data Design
Test Data on Demand
UI - Load Test Harness
Test Mart
Find and Reserve
REQUEST/RESPONSE POOLS
VERSION CONTROLEXPECTED RESULTS
REGRESSION PACKS
DATA DRIVEN TEST PACKS
TEMPLATES
Agile-DataHP ALM/QC
Automation
Data Visualization
Javelin –Data Orchestration

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CA-Test Data Manager Deployment & Process
Adabas
z/Series
IMSDB2
VSAM
Unisys
DMSII
LUW
Informix
MSSQL
Oracle
Teradata
DB2/400 Flat Files
(CSV, Excel,
XML etc.)
i/Series
Windows UI
ODBC/JDBC
COBOL/JCL
JAVA
SQL
LUW
CA – Test Data Manager
Register & Profile
Define Test Data
Engineering Operation
Process Run-time
Enable as
On-Demand Request
Test Data
On-Demand
IMGR & Service Layer
Oracle or SQL
Server
Repository

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Secure
Masked
Subsets of
Data
Mainframe
Environment
DB2 z/OS
VSAM
IMS
non-indexed flat files
Other DBs
Validation
Profiling
PII Discovery
Data Subset Definition
Built-In Masking Functions
Synthetic Data Rules
Rules and
Definitions
Meta Data
Secure and effective test data engineering on Mainframe
CA TDM
Windows Workbench
Data never has to leave the Mainframe
Mainframe
Batch Operation
Rules
Repository
(Rules and Meta Store)
PDS
JCL & CTL)
FTP
ODBC/Copybooks

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End-End Integrity
§ Across multiple source (and platforms!)
– z/Series: DB2, IMS, VSAM (KSDS, ESDS, etc.) or flat files (PS) etc.
– i/Series – AS400: DB2/400
– Distributed: Oracle, SQL Server, DB2 UDB etc.
– File structures: CSV, Excel, XML, SOAP, REST, JSON etc.
Maintaining database, business and logical integrity
CustID Item# Order Date
07221 80-25442 2015-01-25
07221 80-25442 2015-03-14
CustID Item# Order Date
10002 80-25442 2015-02-15
10002 80-25442 2015-05-19
Data retains structure, context and referential integrity but does not contain “real” information.
CustID Name SSN Street
08052 Andrew Chapman 181-23-7171 87 Featherbed Lane
09112 Brian James 112-32-4435 12 Park Lane
08052 Andrew Chapman 181-23-7171 87 Featherbed Lane
07221 Tom Angle 771-11-8972 8 Boardwalk
CustID Name SSN Street
10000 Hikaru Sulu 545-95-1245 87 Featherbed Lane
10001 Nyota Uhura 802-41-3411 12 Park Lane
10000 Hikaru Sulu 545-95-1245 87 Featherbed Lane
10002 Pavel Chekov 325-74-6517 8 Boardwalk

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Project and Version Centralized Control
§ Re-usable assets in a Test Data
Warehouse
§ Provides a central library of test data
engineering operations
§ Assets can be upgraded across versions
(releases)
§ Regression packs can be stored
CA Test Data Manager Test Data Warehouse (Repository)

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Automatically discover sensitive data stored enterprise-wide
§ Powerful, mathematically-based
algorithms discover and tag all sensitive
data, enterprise-wide
§ Simple, easy to use filtering by tag when
selecting columns or tables to mask
§ Support for every major database type,
mainframe platforms and flat files
CA Test Data Manager Data Profiler

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Subsetting: data sets are extracted based on exact criteria
CA Test Data Manager Data Subset
Referentially intact subsets of data
Production Attributes
Scenario Subset
Covered Subset
Production & Enhanced
Attributes

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De-sensitize millions of rows of referentially intact data in minutes
CA Test Data Manager Fast Data Masker
These include substitution, randomization, hashing and seed
data. Once created, masking rules can be stored and re-used
from a central repository.
GT Fast Data Masker will only display suitable masking routines,
based on the selected column and tables
Deterministic masking functions and built-in cross-referencing
ensure consistency. The referentially intact, realistic data, can be
injected into multiple systems at once
Number of Rows Time to Mask
201,722,392 9 minutes, 42 seconds
453,877,152 11 minutes, 22 seconds
768,088,071 7 minutes, 57 seconds
17,422,541 1 minute
46,579,485 1 minute, 25 seconds
1,759,612 13 seconds
47, 895 5 seconds
GT Fast Data Masker uses native database scripts to
produce the highest possible performance when masking
Oracle, MS Server, Teradata and mainframe platforms
§ Mask millions of rows of data in
minutes using native masking engines
§ Over 80 built-in data masking functions
§ Replacements with realistic,
referentially intact, applied to multiple
systems
§ Demonstrate Compliance with the EU
GDPR, GLBA, HIPAA, PCI DSS, PIPEDA,
and more

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Generate Required Data Scenarios
CA Test Data Manager Synthetic Data Generation
This means that new data can efficiently and quickly
injected into existing databases for exactly the
requirements you need
§ Extensive algorithms for Synthetic data applied
in a Data Painter style
§ Use Symbolic values, combine functions,
include conditional values and mine data from
other sources into the new data
§ Publish (inject/load) single data object as
multiple/varied new rows in existing database
§ Store data objects centrally, as re-usable assets
in a Test Data Warehouse
§ Data can be re-used across versions and
releases, with versioned regression packs

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Find & Reserve Existing Data
CA Test Data Manager Test Matching
SAP-1
Data Attribute
Mining
Data Test
Conditions
Datamaker Test Matching
DB-2
XML
Matched Test
QTP Scripts

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Combine TDM Operations to Provide Comprehensive Set of Data
Referential Integrity
Profiling
PII Discovery
Native DB Engines
Seed Replacement
Audit
CA Test Data Manager
1 2 3
Secure Data
Subsets
Test/Dev
Environments
Gold Copy
Subset
Seed
Tables
Built-in Masking
Functions
Cross
Referencing
Masked
Generated Scenarios
Find & Reserve Data
Synthetic
Content
Synthetic
Content
Production data sources and files
XML Files
Excel
Files
SQL Files
CSV Files Fixed Definition
Files
HTML Files
VSAM/ISAMSwift
TXT Files
Test
Data
Warehouse

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Avoid costly mistakes when managing z/OS Test Data
Avoid use of raw production data is a mandatory operation for modern organizations. The average cost per
incident of data breach worldwide is $3.5m. In the US, the risk was as high as $5.85m1.
In as many as 59% of these incidents, the risk could have been mitigated by implementing Test Data
Management best practices
1
Ponemon Institute, 2014
CA TDM allows users to apply Test Data Management practices directly on the mainframe reducing the
reliance on slow, expensive Extract, Transform and reload technologies to ensure compliance and take
advantage of fit-for-purpose test data.
Processing data directly on the mainframe can provide teams with millions of rows of high quality,
meaningful, referentially intact data, in a short period of time and at less risk to quality!

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Recommended Sessions
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Must See Demos
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Mainframe Theater

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Questions?

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Thank you.
Stay connected at communities.ca.com

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Mainframe and Workload Automation
For more information please visit ca.com/appdev
Prashant Pandey
Sr Principal Consultant, Presales
prashant.pandey@ca.com