acceletest
TRANSCRIPT
Fast, Easy & Compliance Approved!
Are you challenged by refreshing QA Test Data? Is the process of building QA test data complex and time
consuming? Are you concerned about risk associated with data privacy in
your test environment?If you answered YES to any of these questions then, AcceleTest is most likely your answer.
Other tools on the market don’t come close to the power and speed of AcceleTest.
The challenge!
Generate artificial data• Difficult to capture all of the corner cases of production data
• Requires deep technical and functional knowledge to generate reasonable data
Use production data• Have to protect customer information (Name, SSN, etc.)
• Usually don’t want full volume
There is the need for test data that reflects the complexity of real data.
A test data management solution can provide fast, efficient and secure sourcing and masking of production data for testing.
How do you create test data?
AcceleTest is an enterprise solution designed to work within large-scale and complex data testing environments for simple and secure sourcing and masking of production data for testing.
Core Capabilities: Subset: AcceleTest employs a requirements driven rules engine to create
real test data from complex production systems. Simultaneously subset, transform and protect using a single automated, repeatable process.
Consistent Data Masking: De-identify and secure data “in flight” as part of a unified data extraction workflow. Consistently mask related data.
Compare: Automated comparison of expected vs. actual results. Quickly identify differences in data sets through web based reports.
What is AcceleTest?
AcceleTest is a complete solution designed specifically to work within complex data testing environments, making it simple to efficiently and securely test with live production data.
AcceleTest is designed for use by testers and business analysts. You don’t need expensive technical resources to manage the creation of test data.
AcceleTest takes the complexity out of right-sizing your production data, with rules based technology to intelligently create subsets of production data that meet all of your test criteria.
AcceleTest accelerates the testing process through automated data comparison that ensures differences in data sets can be quickly identified and properly analyzed.
Why AcceleTest?
AcceleTest improves efficiency by helping to implement automated and repeatable processes for the creation and management of data subsets.
AcceleTest maintains the referential relationships within and across databases during the masking process.
Why AcceleTest?
SourceProduction
Obfuscated
Secure
Customer
TargetDev or Test
SubsetDe-IdentifyCompare
AcceleTest is designed to work in complex data environments.
High Volume Processing
– Parallel execution of masking rules
– Automatically spins up threads based on the number of CPU cores
– Develops a “plan” for loads based on RI constraints or the lack thereof
– Performs dependency analysis and keeps a work queue that prioritizes based on a topographical sort
– Minimizes table locks by avoiding long running transactions
– B-Trees for fast search during sub-setting and masking
Why AcceleTest?
AcceleTest is designed to work in complex data environments.
Security
– Cryptographic hash is used to avoid storing sensitive data within AcceleTest
– Masks the data “in-flight” to avoid landing sensitive data outside of production
Complex data
– Auto wiring to automatically import defined intra-database relationships
– Support for self-referencing tables
– Support for multiple foreign key relationships in the same table
– Support for multi-part foreign key
– Database specific SQL
Why AcceleTest?
Subsetting of complex databases is hard. Few people can write queries to subset a 1000 table database.
Proper subsetting results in smaller test environments and faster test cycles.
Takes the complexity out of creating referentially intact subsets of production data.
Rules-based engine creates meaningful subsets of production data that are automatically generated after an easy one-time setup.
Due to the complexity of databases, AcceleTest has implemented the concept of utilizing a “driving” dataset that is in turn utilized to fan out across the database to perform the subset.
Subset
Subset data while maintaining referential integrity – for example pull customers and everything related to that set of customers only.
Subset with Advanced RI
Account Type
Acct_Type_Code
Acct_Type_Desc
Customer
Cust_Num
Cust_Name
Cust_Addr
Account
Acct_Num
Cust_Num
Acct_Type_Code
Transaction Type
Tran_Type_Code
Tran_Type_Desc
Acct Tran
Acct_Num
Date
Amount
Tran_Type_Code
Driving Data Set
Consistent Data Masking De-identifies and secures data “in flight” as part of unified data extraction
workflow. Consistently masks data within and across databases, retaining referential
integrity. Proprietary rules engine irreversibly de-identifies the data, transforming
sensitive data in a way that cannot be reverse engineered.
Consistent Data Masking Supported De-Identification Methods:
• Random • Random with Mask• Hash• Domain List• SSN Conformant• Percent Variance• Date Variance• Custom Script (Lua)
Preserve referential integrity while de-identifying data.
De-Identify with Advanced RI
AcctNum Name123456 John Smith
AcctNum Date Amount
123456 2/01/2016 56.78
Account Table
Transaction Table
Source Data
AcctNum Name
7659 James Jones
AcctNum Date Amount
7659 2/01/2016 56.78
Account Table
Transaction Table
Target Data
Maintain referential integrity across different databases.
De-Identify with Advanced RI
AcctNum Name123456 John Smith
AcctNum Date Amount
123456 2/01/2016 56.78
Account Table
Transaction Table
Source Database A
AcctNum Name
7659 James Jones
AcctNum Date Amount
7659 2/01/2016 56.78
Account Table
Transaction Table
Target Database A
Source Database B
AcctNum Name
123456 John Smith
Account Table
Target Database B
AcctNum Name
7659 James Jones
Account Table
Designed to be used by non-technical users Compare same or different database types Compare anywhere from single tables to entire databases at once Automated field mappings Shows field by field differences Web-based reporting Compares can be saved and run on a scheduler as a regular set of jobs
Compare
Compare to determine records added, changed or deleted.
Compare
BaseLine(Before)
Target(After)
Compare Results
1 Sophia Sawyer2 Lana Turner3 John Wayne4 James Stewart
1 Wilma Flintstone3 John Wayne5 Barnie Rubble
Compare Report
Summary:Added Deleted Changed 1 2 1
Details:Added Records 5 Barnie Rubble
Deleted Records 2 Lana Turner 4 James Stewart
Changed Records 1 Sophia SawyerChanged to 1 Wilma Flintstone
De-Identify Customer InformationNameAddress (related fields - city, state, zip)Phone Number (keep area code)
Age Transaction DataTransform Transaction Date
Subset based on Account TypeSelect Checking Accounts with Withdrawals
Client Specific Banking Scenarios
