Conquering the challenges of Data Warehouse ETL Testing

1
Conquering the challenges of Data Warehouse ETL
Testing
2
Introduction ETL stands for Extract-Transform-L
oad and is a typical process of loading data from
a source system to the actual data warehouse and
other data integration projects. It is important
to know that independent verification and
validation of data is gaining huge market
potential. Many organizations and companies are
now thinking of implementing ETL and Data
warehouse processes as they realize that valid
data in production is critical for making
informed business decisions.  Importance of Data
Warehouse for organizations Organizations with
already welldefined IT practices are at an
innovative stage, leading the next level of
technology transformation by constructing their
own data warehouse to store and monitor real-time
data. However, such organizations realize that
testing the data is business-critical as it
ensures the data collected is complete, accurate,
and valid. They also understand the fact that
comprehensive testing of data at every point
throughout the ETL process is important and
inevitable, as more of this data is being
collected and used for strategic decision-making
that impact their business forecasting
capabilities. But certain strategies that
are being followed currently are time-consuming,
resource-intensive, and inefficient. A well-planne
d and effective ETL testing scope guarantees
smooth conversion of the project to the final
production phase. Now, let us see some of the
issues that are common with ETL and Data
Warehouse testing. 
3

• Some of the important ETL testing
  challenges are 
• Unavailability of inclusive test bed at times 
• Lack of proper flow of business information 
• Loss of data might happen during the ETL process 
• Existence of several ambiguous software
  requirements 
• Existence of apparent trouble in acquiring and
  building test data 
• Production sample data is not a true
  representation of all possible business
  processes 
• Some of the important issues with Data
  Warehouse testing are 
• Data Warehouse/ETL testing requires SQL
  programming. As most of the testers usually have
  limited SQL coding skills, it makes data testing
  very difficult 
• Performing Data  completeness checks
  for the transformed columns is tricky 
• Certain testing strategies used are time
  consuming 

4

Types of ETL Testing  Data is important for all
businesses to make critical decisions. ETL
testing plays a significant role in verifying,
validating, and ensuring that the business
information is exact, consistent, and reliable.
ETL Testing is datacentric testing, which
involves comparing large volumes of data across
heterogeneous data sources. This datacentric
testing helps in achieving highquality data by
getting the erroneous processes fixed quickly and
effectively.  Data-centric Testing?Data-centric
testing revolves around testing the quality of
data. The objective of data-centric testing is to
ensure that valid and correct data is in the
system. It ensures that proper ETL processes are
applied on source database, during
transformation, and on load data in the target
database. It further ensures that proper system
migration and upgrades are performed.  Data accur
acy testing?This type of testing ensures that
the data is accurately transformed and loaded as
expected. Through this testing, we can identify
errors obtained due to truncation of characters,
improper mapping of columns, implementation
errors in logic, etc.  Data completeness testing
?These tests help to verify that all the expected
data is loaded in target from the source. It
helps to verify the count of rows in driving
table matches with the counts in the target
table.  Read Full Blog at https//www.cigniti.co
m/blog/conquering-the-challenges-of-data-warehouse
-etl-testing/
5