Rajesh and Dipen delivered a session on "The Indispensable role of Testing in Business Intelligence projects" at ATAGTR2020 ATAGTR2020 was the 5th Edition of Global Testing Retreat. Rajesh began his career as a Business Intelligence Analyst in 2010 and then progressed in building complex and high-volume data warehouse for Analytics. Dipen has 5 years of experience in the Analytics field to build the solution and BI report testing with agile mind-set. The video recording of the session is now available on the following link: https://youtu.be/3pjpRfiKtaA To know more about #ATAGTR2020, please visit: https://gtr.agiletestingalliance.org/

1. #ATAGTR2020
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The Indispensable role of Testing
in Business Intelligence projects
Rajesh Gaddipati
Dipen Raval

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#ATAGTR2020
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Agenda:
 Retail data is detail data and increase in customer
expectations to cope with competitors at global
market.
 Relationship of better built Decision support
system and Retail industry.
 Business Intelligence Testing strategies
 Multiple consequences of not having proper
Testing strategy
 Tabjolt for Tableau
 Indispensable role of Testing for any successful BI
project

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#ATAGTR2020
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Retail is Detail
 Intensive data in Retail industry at global level.
 Traditional Decision Support Systems in Retail
industry.
 Increased retail customer expectations from
decision support systems with extensive data
availability.
 Embracing multiple Business Intelligence tools for a
better DSS.
 Key role of quality data

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#ATAGTR2020
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Retail data in Water industry
 Fragmented Water Utility Sector for infrastructure bids.
 Treatment plant equipment
 Tanks
 Sewer Services
 Pumps
 Pipe
 Construction contractors
 Chemicals
 Well System & equipment
 Valves
 Misc. Services
 Need of predictive and prescriptive analytics with data
quality
 Growing demand for asset management with data
governance due to extensive data integrations.

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#ATAGTR2020
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Traditional BI Testing Strategies and challenges:
 Level of trust on data is directly proportional to the success of any Business intelligence project.
 Testing is not an option and it cannot be avoided.
 Traditional strategy of testing in BI projects can be segregated in 4 phases with generic test cases:
1. ETL Flow tests
2. Tests with Staging data
3. Data loading to Central Warehouse
4. BI Report testing
 Extensive manual tests for complex validations.
 BI reconciliation.
 Time consuming root cause analysis.
 Wrong choice and effects of depending on application developers for generic tests.

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#ATAGTR2020
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Agile nature strategic testing
 Proper roadmap for planning and execution to validate critical business rules.
 Categorizing BI testing into multiple segments, to be able to pinpoint data defects.
1. Data at Source level:
a. Data Validation tests from multiple sources.
2. Data at Staging level:
a. Reconciliation checks between source and target
b. ACID compliance checks
c. Functional testing of flows
3. Data at Warehouse level:
a. Reconciliation checks between source and target
b. Performance testing on load sequences
c. Security tests
4. BI Report testing:
a. Functional testing
b. Report Performance Testing
c. Regression testing
d. Stress testing
e. Security tests

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#ATAGTR2020
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Report Performance & Scalability testing:
 Performance issues are very common in BI implementation
projects.
 Strategy for performance testing should be in a very broad way.
 Identifying bottlenecks for report performance is crucial before
dashboards are live.
 Accessibility of reports should be tested under high traffic while
sourcing data from multiple data marts.
 Sudden spikes generated due to accessing same report or
dashboard should be analyzed.
 Surge in thinktime of a particular dashboard and it s impact on
other linked reports, endurance test.
 Volume testing to analyze reporting application performance
under varying database volumes.

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#ATAGTR2020
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Consequences of ignoring Testing
 Two real scenarios that can be easily faced:
1. Missing of transformation rules between source and target.
a. Not all transformation rules are thoroughly tested by application developers due to limitations.
b. Discrepancy in models at security level of data, which can lead to outage in production.
c. Difficult to identify crucial transformation rules, just by analyzing only a set of data during report creation by
developers.
2. Volume test can always be a challenge:
a. Impossible to validate on large scale, while working only on a single module by developers, where that
module can be interlinked with other models and to provide analytics.
b. Historical results storing is crucial to provide predictive and prescriptive analytics, which is impossible
without a proper testing Software as part of BI project.

9. #ATAGTR2020
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#ATAGTR2020
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2020 Gartner BI Magic Quadrant:
 Microsoft & Tableau – Two BI Leaders who
retain their place continuously in
Quadrants Leaders section.
 Both the leaders have specifically
designed tools for testing.
Microsoft – Microsoft Test Manager
Tableau - Tabjolt
Source: https://www.analyticsvidhya.com/blog/2020/02/gartners-2020-magic-quadrant-bi-analytics-tools/

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#ATAGTR2020
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Tableau - Tabjolt
 TabJolt is a “Point and Run” load and performance testing tool specifically designed for Tableau Server.
 When to use Tabjolt?
1. I want to deploy a brand new Tableau Server. How will the new server scale in my
environment, on my hardware and my workload?
2. I am moving from Tableau Server 10.5 to version 2018.x. Given my hardware, workbook and
environment, how will 2018.x scale in my environment?
3. I want to find the best server deployment configuration. Given my hardware, workbooks and
environments, how can I find the configuration that works best for our deployment?
4. In IT, it takes me 6-8 weeks to complete the required load test cycle for production go-live
(deployment) of Tableau Server. How can I reduce the time it takes to complete the production
go-live testing?
Source: https://github.com/tableau/tabjolt

11. #ATAGTR2020
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#ATAGTR2020
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Tabjolt Architecture:
 Specific to Tableau Server
 Based on Apache JMeter
 Scaling Considerations:
1.Environment: Topology
2. Variability in Data sources
3. Workload of workbooks & peak users
4. Think time
 Easy install
Source: https://www.tableau.com/about/blog/2015/4/introducing-tabjolt-point-and-run-load-testing-solution-tableau-server-38604

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#ATAGTR2020
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Single Dashboard Analysis:
Source: https://public.tableau.com/profile/rajesh.gaddipati#!/vizhome/H2Ostory/LifeExpectancyandHIVdeathsacross

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#ATAGTR2020
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Understanding TabJolt Config:
 Install Directory:  Config:
 vizpool:
 Passing Dashboard name that will be used for testing into “vizpool” and credentials through “userpool” .csv
files from Config folder.
 userpool:

14. #ATAGTR2020
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#ATAGTR2020
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Understanding TabJolt Config:
 PerfTestConfig.yaml:
Information on PostgreSQL DB details can be edited here.
“thinkTime” details can be provided here based on testing needs.

15. #ATAGTR2020
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#ATAGTR2020
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Test Plans:
Tabjolt provides default test plans (.JMX) that can be executed based on type of usage of dashboards or
reports:
1. InteractVizLoadTest – Dashboards that are mostly for purpose of interaction.
2. ViewInteractVizLoadTest – Dashboards that are both viewed and interacted by users.
3. ViewVizLoadTest – Dashboards which are only for the purpose of viewing with less interaction.
Create your own test plan based on workbooks, models and given thinktime’s.
Execute any plan , just by specifying it on executing “go” batch file
“C:Tabjolt>go –t-testplansInteractVizLoadTest.JMX –d-500 –c*5
Duration: 500sec “d”
Threads : 5 “c”

16. #ATAGTR2020
As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial
channels(Provided due credit is given to me/us)
#ATAGTR2020
As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial
channels(Provided due credit is given to me/us)
Test results from PerformanceViz.tbw :
 Results to analyse KPI’s and diagnose bottlenecks in topology, and drill down of test results in detail

17. #ATAGTR2020
As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial
channels(Provided due credit is given to me/us)
#ATAGTR2020
As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial
channels(Provided due credit is given to me/us)
Try Tabjolt:
 Tableau Server Download & Install.
https://www.tableau.com/support/releases/server/2020.3
 Download dashboard for Single-Dashboard Analysis.
https://public.tableau.com/profile/rajesh.gaddipati#!/vizhome/H2Ostory/Safewatercrisis
 Download & Install Tableau Desktop.
https://www.tableau.com/products/desktop/download
 Data Source used in Dashboard.
Life Expectancy 2019:
https://drive.google.com/file/d/1x3C3cNkW6KFB7CqgF7_rCvoZwaEmNFA0/view?usp=sharing
Water Crisis:
https://drive.google.com/file/d/1a8YrTRrGv3roc2Jlh8SQn1SvXQoZL7OC/view?usp=sharing
 Download and Install Tabjolt.
https://github.com/tableau/tabjolt/releases

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channels(Provided due credit is given to me/us)
#ATAGTR2020
As a author of this presentation I/we own the copyright and confirm the originality of the content. I/we allow Agile testing alliance to use the content for social media marketing, publishing it on ATA Blog or ATA social medial
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Indispensable role of Testing in BI Projects
 Dedicated Testing team will be able to address ownership at all phases of BI testing.
 Tools like TabJolt can rapidly generate test plans for any scenarios.
 Helping to validate large volumes of data instantly with data security
 Not only diagnosing bottlenecks in our environment, but predictive and prescriptive analytics can be
implemented, as we are able to store historical results.
 Performance issues that were common across BI projects are well addressed and pin point, that may
be due to design or topology.
 Great strategy for Capacity Planning.
 Customers level of trust in data is key factor for any successful BI project, which cannot be achieved
without Testing.

19. #ATAGTR2020
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#ATAGTR2020
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THANK YOU