Data Warehouse Testing
Content
Data Warehouse Testing
By : Kartikey Brahmkshatriya (M.C.A)
Index ...............................................................................................................................................2 Introduction.............................................................................................................................3 About Data Warehouse...........................................................................................................3 Data Warehouse definition.................................................................................................3 Testing Process for Data warehouse:......................................................................................3 Requirements Testing :......................................................................................................3 Unit Testing : .................................................................................................................... Integration Testing : .......................................................................................................... !cenarios to be co"ered in Integration Testing...............................................................# $a%idating the Re&ort data..............................................................................................' User Acce&tance Testing....................................................................................................' (onc%usion..............................................................................................................................'
Introduction This document details the testing process involved in data warehouse testing and test coverage areas. It explains the importance of data warehouse application testing and the various steps of the testing process. A out Data Warehouse Data warehouse is the main repository of the organization's historical data. It contains the data for management's decision support system. The important factor leading to the use of a data warehouse is that a data analyst can perform complex queries and analysis (data mining) on the information within data warehouse without slowing down the operational systems. Data Warehouse definition • Subject-oriented : u!"ect #riented $Data warehouses are designed to help you analyse data. %or example& to learn more a!out your company's sales data& you can !uild a warehouse that concentrates on sales. 'sing this warehouse& you can answer questions li(e )*ho was our !est customer for this item last year+) This a!ility to define a data warehouse !y su!"ect matter& sales in this case& ma(es the data warehouse su!"ect oriented. The data is organized so that all the data elements relating to the same real$ world event or o!"ect are lin(ed together. Integrated : Integration is closely related to su!"ect orientation. Data warehouses must put data from disparate sources into a consistent format. The data!ase contains data from most or all of an organization's operational applications and is made consistent. Time-variant : The changes to the data in the data!ase are trac(ed and recorded to produce reports on data changed over time. In order to discover trends in !usiness& analysts need large amounts of data. , data warehouse's focus on change over time is what is meant !y the term time variant. Non-volatile : Data in the data!ase is never over$written or deleted& once committed& the data is static& read$only& !ut retained for future reporting. #nce entered into the warehouse& data should not change. This is logical !ecause the purpose of a data warehouse is to ena!le you to analyse what has occurred.
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•
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Testing !rocess "or Data #arehouse: Testing for a Data warehouse consists of requirements testing& unit testing& integration testing and acceptance testing.
Requirements Testing : The main aim for doing -equirements testing is to chec( stated requirements for completeness. -equirements can !e tested on following factors. .. 0. 1. 2. 3. ,re ,re ,re ,re ,re the the the the the requirements requirements requirements requirements requirements /omplete+ ingular+ ,m!iguous+ Developa!le+ Testa!le+
In a Data warehouse& the requirements are mostly around reporting. 4ence it !ecomes more important to verify whether these reporting requirements can !e catered using the data availa!le. uccessful requirements are those structured closely to !usiness rules and address functionality and performance. These !usiness rules and requirements provide a solid
foundation to the data architects. 'sing the defined requirements and !usiness rules& high level design of the data model is created. #nce requirements and !usiness rules are availa!le& rough scripts can !e drafted to validate the data model constraints against the defined !usiness rules. Unit Testing : 'nit testing for data warehouses is *4IT56#7. It should chec( the 5T8 procedures9mappings9"o!s and the reports developed. This is usually done !y the developers. 'nit testing will involve following .. 0. 1. *hether 5T8s are accessing and pic(ing up right data from right source. ,ll the data transformations are correct according to the !usiness rules and data warehouse is correctly populated with the transformed data. Testing the re"ected records that don:t fulfil transformation rules.
Integration Testing : ,fter unit testing is complete& it should form the !asis of starting integration testing. Integration testing should test out initial and incremental loading of the data warehouse. Integration testing will involve following .. 0. 1. 2. 3. equence of 5T8s "o!s in !atch. Initial loading of records on data warehouse. Incremental loading of records at a later date to verify the newly inserted or updated data. Testing the re"ected records that don:t fulfil transformation rules. 5rror log generation.
The overall Integration testing life cycle executed is planned in four phases; -equirements 'nderstanding& Test <lanning and Design& Test /ase <reparation and Test 5xecution.
Business Business &e'uirement &e'uirement Document(&e'uirement Document(&e'uirement Tracea Tracea i)ity i)ity Matrix Matrix
*igh *igh +e%e) +e%e) Design Design document document
@, Team -eviews 6-D for completeness. @, Team !uilds Test <lan
&e'uirements &e'uirements Testing Testing &e%ie# &e%ie# o" o" *+D *+D
Develop Test /ases and @8 @ueries
Test Test Case Case !re,aration !re,aration
.nit .nit Testing Testing -unctiona) -unctiona) Testing Testing
Test 5xecution
&egression &egression Testing Testing !er"ormance !er"ormance Testing Testing
.ser .ser Acce,tance Acce,tance Testing Testing (.AT) (.AT)
<rocess for Data warehouse Testing
$cenarios to e co%ered in Integration Testing
Integration Testing would cover 5nd$to$5nd Testing for D*4. The coverage of the tests would include the !elow; 1. Count Validation $ -ecord /ount =erification D*4 !ac(end9-eporting queries against source and target as a initial chec(. Source Isolation $ =alidation after isolating the driving sources. Dimensional nal!sis $ Data integrity !etween the various source ta!les and relationships. Statistical nal!sis $ =alidation for various calculations. Data $ualit! Validation $ /hec( for missing data& negatives and consistency. %ield$!y$%ield data verification can !e done to chec( the consistency of source and target data. &ranularit! $ =alidate at the lowest granular level possi!le (8owest in the hierarchy 5.g. /ountry$/ity$ treet > start with test cases on street). (t)er validations $ ?raphs& lice9dice& meaningfulness& accuracy
2. 3. ". #.
%.
'. .
/a)idating the &e,ort data #nce the 5T8s are tested for count and data verification& the data !eing showed onto the reports hold utmost importance. @, team should verify the data reported with the source data for consistency and accuracy. .. =erify -eport data with source $ ,lthough the data present in a data warehouse will !e stored at an aggregate level compare to source systems. 4ere the @, team should verify the granular data stored in data warehouse against the source data availa!le. %ield level data verification $ @, team must understand the lin(ages for the fields displayed in the report and should trace !ac( and compare that with the source systems. /reating @8s $ /reate @8 queries to fetch and verify the data from ource and Target. ometimes it:s not possi!le to do the complex transformations done in 5T8. In such a case the data can !e transferred to some file and calculations can !e performed.
0.
1.
User Acceptance Testing 4ere the system is tested with full functionality and is expected to function as in production. ,t the end of ',T& the system should !e accepta!le to the client for use in terms of 5T8 process integrity and !usiness functionality and reporting.
Conc)usion 5volving needs of the !usiness and changes in the source systems will drive continuous change in the data warehouse schema and the data !eing loaded. 4ence& it is necessary that development and testing processes are clearly defined& followed !y impact$analysis and strong alignment !etween development& operations and the !usiness.
By : Kartikey Brahmkshatriya (M.C.A)
Index ...............................................................................................................................................2 Introduction.............................................................................................................................3 About Data Warehouse...........................................................................................................3 Data Warehouse definition.................................................................................................3 Testing Process for Data warehouse:......................................................................................3 Requirements Testing :......................................................................................................3 Unit Testing : .................................................................................................................... Integration Testing : .......................................................................................................... !cenarios to be co"ered in Integration Testing...............................................................# $a%idating the Re&ort data..............................................................................................' User Acce&tance Testing....................................................................................................' (onc%usion..............................................................................................................................'
Introduction This document details the testing process involved in data warehouse testing and test coverage areas. It explains the importance of data warehouse application testing and the various steps of the testing process. A out Data Warehouse Data warehouse is the main repository of the organization's historical data. It contains the data for management's decision support system. The important factor leading to the use of a data warehouse is that a data analyst can perform complex queries and analysis (data mining) on the information within data warehouse without slowing down the operational systems. Data Warehouse definition • Subject-oriented : u!"ect #riented $Data warehouses are designed to help you analyse data. %or example& to learn more a!out your company's sales data& you can !uild a warehouse that concentrates on sales. 'sing this warehouse& you can answer questions li(e )*ho was our !est customer for this item last year+) This a!ility to define a data warehouse !y su!"ect matter& sales in this case& ma(es the data warehouse su!"ect oriented. The data is organized so that all the data elements relating to the same real$ world event or o!"ect are lin(ed together. Integrated : Integration is closely related to su!"ect orientation. Data warehouses must put data from disparate sources into a consistent format. The data!ase contains data from most or all of an organization's operational applications and is made consistent. Time-variant : The changes to the data in the data!ase are trac(ed and recorded to produce reports on data changed over time. In order to discover trends in !usiness& analysts need large amounts of data. , data warehouse's focus on change over time is what is meant !y the term time variant. Non-volatile : Data in the data!ase is never over$written or deleted& once committed& the data is static& read$only& !ut retained for future reporting. #nce entered into the warehouse& data should not change. This is logical !ecause the purpose of a data warehouse is to ena!le you to analyse what has occurred.
•
•
•
Testing !rocess "or Data #arehouse: Testing for a Data warehouse consists of requirements testing& unit testing& integration testing and acceptance testing.
Requirements Testing : The main aim for doing -equirements testing is to chec( stated requirements for completeness. -equirements can !e tested on following factors. .. 0. 1. 2. 3. ,re ,re ,re ,re ,re the the the the the requirements requirements requirements requirements requirements /omplete+ ingular+ ,m!iguous+ Developa!le+ Testa!le+
In a Data warehouse& the requirements are mostly around reporting. 4ence it !ecomes more important to verify whether these reporting requirements can !e catered using the data availa!le. uccessful requirements are those structured closely to !usiness rules and address functionality and performance. These !usiness rules and requirements provide a solid
foundation to the data architects. 'sing the defined requirements and !usiness rules& high level design of the data model is created. #nce requirements and !usiness rules are availa!le& rough scripts can !e drafted to validate the data model constraints against the defined !usiness rules. Unit Testing : 'nit testing for data warehouses is *4IT56#7. It should chec( the 5T8 procedures9mappings9"o!s and the reports developed. This is usually done !y the developers. 'nit testing will involve following .. 0. 1. *hether 5T8s are accessing and pic(ing up right data from right source. ,ll the data transformations are correct according to the !usiness rules and data warehouse is correctly populated with the transformed data. Testing the re"ected records that don:t fulfil transformation rules.
Integration Testing : ,fter unit testing is complete& it should form the !asis of starting integration testing. Integration testing should test out initial and incremental loading of the data warehouse. Integration testing will involve following .. 0. 1. 2. 3. equence of 5T8s "o!s in !atch. Initial loading of records on data warehouse. Incremental loading of records at a later date to verify the newly inserted or updated data. Testing the re"ected records that don:t fulfil transformation rules. 5rror log generation.
The overall Integration testing life cycle executed is planned in four phases; -equirements 'nderstanding& Test <lanning and Design& Test /ase <reparation and Test 5xecution.
Business Business &e'uirement &e'uirement Document(&e'uirement Document(&e'uirement Tracea Tracea i)ity i)ity Matrix Matrix
*igh *igh +e%e) +e%e) Design Design document document
@, Team -eviews 6-D for completeness. @, Team !uilds Test <lan
&e'uirements &e'uirements Testing Testing &e%ie# &e%ie# o" o" *+D *+D
Develop Test /ases and @8 @ueries
Test Test Case Case !re,aration !re,aration
.nit .nit Testing Testing -unctiona) -unctiona) Testing Testing
Test 5xecution
&egression &egression Testing Testing !er"ormance !er"ormance Testing Testing
.ser .ser Acce,tance Acce,tance Testing Testing (.AT) (.AT)
<rocess for Data warehouse Testing
$cenarios to e co%ered in Integration Testing
Integration Testing would cover 5nd$to$5nd Testing for D*4. The coverage of the tests would include the !elow; 1. Count Validation $ -ecord /ount =erification D*4 !ac(end9-eporting queries against source and target as a initial chec(. Source Isolation $ =alidation after isolating the driving sources. Dimensional nal!sis $ Data integrity !etween the various source ta!les and relationships. Statistical nal!sis $ =alidation for various calculations. Data $ualit! Validation $ /hec( for missing data& negatives and consistency. %ield$!y$%ield data verification can !e done to chec( the consistency of source and target data. &ranularit! $ =alidate at the lowest granular level possi!le (8owest in the hierarchy 5.g. /ountry$/ity$ treet > start with test cases on street). (t)er validations $ ?raphs& lice9dice& meaningfulness& accuracy
2. 3. ". #.
%.
'. .
/a)idating the &e,ort data #nce the 5T8s are tested for count and data verification& the data !eing showed onto the reports hold utmost importance. @, team should verify the data reported with the source data for consistency and accuracy. .. =erify -eport data with source $ ,lthough the data present in a data warehouse will !e stored at an aggregate level compare to source systems. 4ere the @, team should verify the granular data stored in data warehouse against the source data availa!le. %ield level data verification $ @, team must understand the lin(ages for the fields displayed in the report and should trace !ac( and compare that with the source systems. /reating @8s $ /reate @8 queries to fetch and verify the data from ource and Target. ometimes it:s not possi!le to do the complex transformations done in 5T8. In such a case the data can !e transferred to some file and calculations can !e performed.
0.
1.
User Acceptance Testing 4ere the system is tested with full functionality and is expected to function as in production. ,t the end of ',T& the system should !e accepta!le to the client for use in terms of 5T8 process integrity and !usiness functionality and reporting.
Conc)usion 5volving needs of the !usiness and changes in the source systems will drive continuous change in the data warehouse schema and the data !eing loaded. 4ence& it is necessary that development and testing processes are clearly defined& followed !y impact$analysis and strong alignment !etween development& operations and the !usiness.
