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Best Practices in Data Best Practices in Data Warehouse TestingWarehouse Testing
Best Practices in Data Best Practices in Data Warehouse TestingWarehouse Testing
By :- Anindya Mookerjea Prasanth Malisetty
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Agenda
Introduction to Data WarehouseWhy require Best Practices in testing DWHPhases in data warehouse testingData warehouse testing goalsTypes of Data Warehouse Testing Generic Challenges
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Introduction to Data WarehouseA data warehouse is a:
subject oriented Integrated Non-Volatile Time variant collection of data in support of
management’s decision.
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Why require Best Practices in testing DWH
How much confident a company can be to implement it’s data warehouse in the market without actually testing it thoroughly ?
How much testing is enough to implement their data warehouse in the market?
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Why require Best Practices in testing DWH
Would it be possible for a company to remain at the top of the business if the bug is detected at the later stage of testing cycles?
“Undoubtedly we need to have in place the best practices in testing a data warehouse application”
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Phases in data warehouse testing
Business understandingTest plan creation Test case creation Test data file for predictionsTest predictions creation Test case execution Deployment in production environment
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Data warehouse testing goalsNo Data lossesCorrect transformation rulesData validationRegression TestingOneshot/ retrospective testingProspective testingView testingSamplingPost implementation
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Types of Data Warehouse Testing
Business FlowOne-Shot / ProspectiveData QualityIncremental LoadPerformanceVersion Production Checkout
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Business Flow’ in Data Warehouse Testing
Understand Business Specification
Create Test Plan/Test Approach
Develop Test CasesAnd Predictions
Business Test Execution Cycle 1
(Initial Load)
Business Test Execution Cycle 2(Incremental Load)
Business Testing Sign off
Data Mocking
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One-Shot/Prospective in Data Warehouse Testing
Business Requirements Specifications
RetrospectiveTesting
Prospective Testing
Sampling of Data
Test Data
Back out Testing
Test Cases and Predictions
Compare and Regression Testing
Correct Analysis
Defect
NO
YES
Sampling of Data
Test Data
Test Cases and Predictions
Compare and Regression Testing
CorrectNO
Production
Source File
MVS
Data Sources
YES
Production Checkout
BA Sign Off
CorrectNO
Defect
Production Checkout Sign Off
YES
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‘Data Quality’ Data Warehouse Testing
Business Requirement Specification
Transformation Rules Testing
Test Data
DQ Testing for Key Fields
IS DQ % > Emergency %
IS DQ % > Threshold %
DQ E-mail And Abort ETL
DQ E-mail
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‘Incremental Load’ in Data Warehouse testing
Complete Initial Load BA Testing
(Cycle 1,Cycle 2)R: TESTER
Mocked Data for new row Insert scenario
(Natural Key not match condition) Change any Natural Key attribute in Existing
data
Mocked Data for existing row update scenario
(Natural Key match condition)Change any Non Natural Key attribute
in Existing data
Data loaded in the table (Initial Load)
R: SE
Source Data
NON Timeline Tables
Mocked data for Incremental
Load Testing in Source file R: TESTER
Mocked Data for do not delete scenarios
(Natural Key and all other Non natural key attribute match condition) Create a new record same as exiting record.
Condition 1
Condition 3
Condition 2
OUTPUT:Keep record as it is. No change in
Load and Update date.
OUTPUT:Existing record should get
updated with new changes. Update Date = Job run date
OUTPUT:New record should be inserted in the table with changed natural
key combination Load dt, Update Date = Job run
date
Compare Before and After results
Raise MQC ticket in MQC and Communicate the issue on open line conference to Version DBA
BA Testing Sign-off
Defects Found
Defects FoundDefects Found
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‘Version’ Data Warehouse Testing
Requirement in the form of Excel sheet
( Version change Sheet )
Validate the requirements with
Business Specification documents
Changes/Corrections Required
No Changes Develop Test Plan
Approach Document
Addition of columns to
objects
(This involves adds to physical tables and drops and recreates for view objects)
Physical Tables Running Describes on for physical tables for adding columns to the tables and
Renames
Views Select 1-5 rows (pre & post version predictions) to verify that columns were added with appropriate default value and appropriate changes
Deletion / Expansion of columns or
Datatype changes to columns
(This involves renames to physical objects, drops and recreates for view objects)
Compare Before and After results
Raise MQC ticket in MQC and
Communicate the issue on open line
conference to Version DBA
BA Testing Sign-off
Defects Found
No Defects
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‘Production Checkout’ Data Warehouse Testing
Testing only valid values get populated into table (as per BRD’s)
Testing default values cross threshold % (for given attributes)
Validates data is not corrupted (environmental factors)
Verification vs. predictions
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Generic Challenges Huge data volume and complexity.Data volume and complexity of data impacts performance & productivity.Scope of testing is broad as it also involves regression of data.There are different Change Requests which have different testing flow and associated rules.The data used for predictions is different and have to be mapped with actual data, based on different business rules.Chances of one scenario, in testing being repeated, is not much frequent.Currently all types of testing including regression is done by hand.
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Comparison of Flow - Manual Vs Automated
All Modules involve regression
Automation Tool Manual Testing
Regression data captured in
automation tool
Regression data captured in excel
format
Code Implementation
Captured data saved in excel
format
Execute Automation
Script
Data ComparisonData Sorting/Formatting
Test Results are published saved in the required format.
Create the query to Concatenate
key fields & extracting data.
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Any Questions?
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