What is ETL testing & how to enforce it in Data Wharehouse

Extraction, Transformation, and Loading are the three tenets that will
help you in checking the efficiency of the data.
Before we go any further, do you know what ELT testing is?
Majority of you I bet don’t know much about it.
And that’s the reason for me to write this blog. Here you will learn all
about ELT testing and its utilization in the data warehouse. So, without
any further ado, let’s get you started.

To check the correctness of data change against the signed off
business necessities and principles:
1. To confirm that expected data is loaded into data mart or data
warehouse center without loss of any data.
2. To approve the precision of compromise reports (if any e.g. if
there should arise an occurrence of examination of the report of
exchanges influenced through to bank ATM – ATM report versus
Financial balance Report).
3. To ensure finish processes meet performance and scalability
requirements.
4. Data security is additionally in some cases some portion of
ETL testing.
5. To assess the reporting efficiency.

ETL stands for Extraction, Transformation, and Loading. ETL Testing is a
sort of testing strategy which must be done with human cooperation
where it needs to test the Extraction, Transformation, and Loading of
information when moved from source to target with respect to the
Business Requirements.
To get data from the source and load it into the data warehouse –a
procedure of duplicating information starting with one database then onto
the next. It also includes checking the data at different stages that are
being utilized source and destination.
Along these lines, the information is first extracted from the Online
Analytical Processing (OLTP) database and changed by the information
distribution center blueprint and after that stacked into the Data
warehouse. Be that as it may, the information could likewise be from a
non-OLTP source.

#Extracting information from outside sources
#Transforming it to fit operational needs (which can incorporate
quality levels)
#Loading it into the end target (operational information store and
data warehouses)

ETL Testing is not quite the same as application testing since it
requires a data-driven testing approach. Some of the challenges in
ETL Testing are:
# ETL Testing includes contrasting of expansive volumes of
information ordinarily a large number of records.
# The information that should be tried is in heterogeneous data
sources (eg. databases, flat files).
# Data is regularly changed which may require complex SQL
questions for looking at the information.
# ETL testing is particularly subject to the accessibility of test
information with various test situations.
In fact that there are slight varieties in the kind of tests that should be
executed for each task, underneath are the most well-known sorts of
tests that should be improved the situation ETL Testing.

Testing, ideally by a free gathering, ought to be embraced to confirm and
approve the ETL procedure, in this way guaranteeing the quality, fulfillment and
heartiness of the information distribution center. There are assortments of
instruments that can be utilized for ETL testing. There are a few levels of testing
that ought to be performed in ETL testing. Some levels of testing are defined
below:-
Requirements Testing:
# Are the requirements finished?
# Are the requirements testable?
# Are the requirements clear (is there any uncertainty)?
Data Validation Testing:
#Guarantee that the ETL application appropriately rejects, replaces with default
esteems and reports invalid data.
# Confirm that information is changed accurately as indicated by framework
prerequisites and business rules
# Look at interesting estimations of key fields between source data and
warehouse data.

Integration Testing:
# Check that ETL functions work with upstream and downstream processes.
# Confirm the underlying heap of records on data warehouse.
# Test error log generation.
Report Testing:
# Check report data with the information source.
# Make SQL questions to confirm source/target data.
# Check field-level data.
Client Acceptance Testing:
# Confirm that the business rules have been met.
# verify that the framework is worthy to the client.

Execution Testing:
# Check that data loads and queries are executed within the timeframe.
# Confirm stack times with different measures of information to anticipate
adaptability.
Regression Testing:
# Guarantee that current functionality remains flawless at whatever point or
new code is implemented.

Besides essentially enhancing coordination, the advantage of utilizing ETL and data
warehouse is accomplishing quicker response time. The use of data distribution
centers permits the exchange and the examination procedures to work
autonomously. This empowers ventures to accomplish more prominent proficiency
both at the source and the data warehouse transactions processing and also faster
and better querying and analysis.
The second huge advantage of using ETL is enhancing overall data quality. The
three-advance procedure of extricating, changing, and stacking empowers ETL
analyzers to audit the rightness of data in each step. Therefore, ETL analyzers can
distinguish and solve data errors where they occur — in the source, in the data
warehouse, or during the transformation process.
Finally, using ETL additionally advances greater organizational and operational
efficiency. The ETL procedure guarantees that changes made to source data,
paying little heed to where the progressions are started, will be reflected in the data
warehouse. This enables diverse branches of ventures to actualize their own
particular ad hoc software or systems while being assured that the data they use
reflect the changes made by other departments. This engages them to take
activities that will profit their specializations while moving the entire organization
forward.

We, at BugRaptors, while performing Web Accessibility testing we are
also implementing thoughts to make sure that we can manage required
tasks and perform accessibility testing by keeping in mind all the positive
and negative scenarios.

What is ETL testing & how to enforce it in Data Wharehouse

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What is ETL testing & how to enforce it in Data Wharehouse