ETL stands for extract, transform, and load. It can consolidate the scattered data for any
organization while working with different departments. It can very well handle the data
coming from different departments.
For example, a health insurance organization might have information on a customer in
several departments and each department might have that customer's information listed in
a different way. The membership department might list the customer by name, whereas
the claims department might list the customer by number. ETL can bundle all this data
and consolidate it into a uniform presentation, such as for storing in a database or data
warehouse.
ETL can transform not only data from different departments but also data from different
sources altogether. For example, any organization is running its business on different
environments like SAP and Oracle Apps for their businesses. If the higher management
wants to take discussion on their business, they want to make the data integrated and used
it for their reporting purposes. ETL can take these two source system data and make it
integrated in to single format and load it into the tables.
Generally the normal testing steps are:
• Requirements Analysis
• Testing Methodologies
• Test Plans and approach
• Test Cases
• Test Execution
• Verification and Validation
• Reviews and Walkthroughs
The main difference in testing a ETL is that we basically involve the
SQL queries in our test case documents. It is vital to test both the initial loads of the Data
Warehouse from the source i.e. when it gets extracted and then updating it on the target
table i.e. the loading step. In specific cases, where trouble shooting is required, we verify
intermediate steps as well.
A defect or bug detection can be appreciated if and only if it is detected early and is fixed
at the right time without leading to a high cost. So to achieve it, it is very important to set
some basic testing rules. They are:
• No Data losses
• Correct transformation rules
• Data validation
• Regression Testing
• Oneshot/ retrospective testing
• Prospective testing
• View testing
• Sampling
• Post implementation
organization while working with different departments. It can very well handle the data
coming from different departments.
For example, a health insurance organization might have information on a customer in
several departments and each department might have that customer's information listed in
a different way. The membership department might list the customer by name, whereas
the claims department might list the customer by number. ETL can bundle all this data
and consolidate it into a uniform presentation, such as for storing in a database or data
warehouse.
ETL can transform not only data from different departments but also data from different
sources altogether. For example, any organization is running its business on different
environments like SAP and Oracle Apps for their businesses. If the higher management
wants to take discussion on their business, they want to make the data integrated and used
it for their reporting purposes. ETL can take these two source system data and make it
integrated in to single format and load it into the tables.
Generally the normal testing steps are:
• Requirements Analysis
• Testing Methodologies
• Test Plans and approach
• Test Cases
• Test Execution
• Verification and Validation
• Reviews and Walkthroughs
The main difference in testing a ETL is that we basically involve the
SQL queries in our test case documents. It is vital to test both the initial loads of the Data
Warehouse from the source i.e. when it gets extracted and then updating it on the target
table i.e. the loading step. In specific cases, where trouble shooting is required, we verify
intermediate steps as well.
A defect or bug detection can be appreciated if and only if it is detected early and is fixed
at the right time without leading to a high cost. So to achieve it, it is very important to set
some basic testing rules. They are:
• No Data losses
• Correct transformation rules
• Data validation
• Regression Testing
• Oneshot/ retrospective testing
• Prospective testing
• View testing
• Sampling
• Post implementation
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