This document contains the testing process involved in data warehouse testing and test coverage areas.

1. Data Warehouse(ETL) Testing
PROCEDURES
Prepared by
Madhu Nepal

2. CONTENT
• 1. Introduction
• 2. About Data Warehouse
• 2.1 Data Warehouse definition
• 3. Testing Roles and resources
• 4. Items to be tested
• 5. Test strategy
• 6. Test Approach
• 7. Schedule
• 8. Test plan approval

3. INTRODUCTION
• This document contains 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.

4. INTRODUCTION cntd..
• 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

5. DWH
• Data Warehouse- Typically a relational database
that is designed for query and analysis…
• Subject oriented— Data stored in subjectwise
• Integrated - data from disparate sources into a
consistent format.
• Time-Variant - Change over time can be analysed
• Non-volatile - never over written, deleted, static

6. Test Phase
• Phase 1:
• Data Validation
• Performance
• Unit
• Functional
• Data Warehouse (internal testing within validating data stage jobs)
• Phase 2:
• Cross-functional process
• Load
• Security
• Data Warehouse (Repository testing and validation)
• Phase 3: Regression
• Phase 4: Business and client Acceptance

7. Data Werehouse Testing
• Requirements testing-
• Unit testing-
• Integration testing
• Acceptance testing
• Requirements testing- completeness, singular,
ambiguous, developable, testable
• Unit testing- Whitebox,
ETL(procedures/mapping/jobs)-> report-developer for
right data, correct transformation-rejected records

8. DWH Testing contd….
• Integration Testing- initial & incremental loading of
DWH to verify newly inserted or updated data,
testing rejected records and error log generation
• Overall Integration– requirements understanding,
test planning and design, test case preparation and
test execution

9. Integration testing cntd..
• Count validation- as initial check records count by queries against
source and target
• Source isolation- validation after isolating the driving sources
• Dimensional analysis– data integrity between various sources
and their relationship
• Statistical analysis- validation for various calculations
• Data quality validation- check for missing data, negative &
consistency, field by field verification by checking consistency of source
and target data
• Granularity- validate lowest in the hierarchy- bottom up
• Other validation- graphs, dice, accuracy, meaningfulness

10. Validation the report
• After ETL testing data must verify for
consistency and accuracy
• 1- verify report data with source
• 2- fields level data verification- link of report & source
• 3- creating SQLs - fetch & verify data from source and target

11. Testing the DW– Manual Testing Flow

12. During ETL testing
• Data warehouse – topdown approach was followed where
first enterprise data werehouse then datamart were created
weekly.
• Extraction- data is taken one or more OLTP system in the form
of Xml, Flat, CoBol, SAP, people soft files from insite and online
were extracted periodically.
• Transforming- a) data were merged to single from different
sources. b) inconsistency and inaccuracy were identified by
cleansing. C) data were derived to new data definition form. D) data
were aggregated to single form.(Overall--removing inconsistencies,
adding missing fields, summarizing detailed data and deriving new
fields to store calculated data.)

13. ETL testing contd…
• Loading- Data were loaded in data warehouse by
incremental loading method..(Mapping and
loading)
• ETL approaches- Landing area is the place where
source files and tables are found. In staging data
validation is done and valid data are loaded in
staging table but invalided data are captured in
error table. Preload- file layer is formed after
complete transformation that gives datamart- will
be inserted, updated from preload files.

14. Functional test
• functional test strategy: Test every entry
point in the system (feeds, databases, internal
messaging, front-end transactions).

15. User Acceptance Testing
• Users know the data best, and their
participation in the testing effort is a key
component to the success of a data
warehouse implementation. The objective of
user acceptance testing is to certify that a
release meets user expectations and is ready
for production.

16. Data source to reporting

17. Regression Testing
• Regression testing is revalidation of existing
functionality with each new release of code
and data. When building test cases, they will
likely be executed multiple times as new
releases are created due to defect fixes,
enhancements or upstream systems changes.
Building automation during system testing will
make the process of regression testing much
smoother.

18. Contd...
• Staff training
Experienced staffs will be hired so that training will not
be needed but project briefing will be done on
12/03/2015.
• Risk and mitigations
One staff will be running staff so that s/he will be
replacement of anyone during leave. Back up of all
work will be kept so that sudden crash of system can
be recovered.
• Approvals
Signature of team managers, QA manager, project
manager