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What Are The Different Types Of ETL Testing ?
What is ETL testing?
ETL is ordinarily connected with Data Warehousing ventures however there in actuality any type of bulk data development from a source to an objective can be viewed as ETL. Large organizations regularly have a need to move application data starting with one source then onto the next for data migration or data integration purposes. ETL testing is a data driven testing procedure to approve that the information has been transformed and stacked into the objective not surprisingly.
Every organization classify testing sorts by their own specific manner in view of the testing practices or testing technique assemble @ organizational level. It remains constant for ETL testing too. Here and there, for bigger programs/projects, it changes from client to client. For the most part, beneath are the principle sorts of testing that fall under ETL testing:
Transformation or logical testing:
To test any logical gaps in the. Here, contingent on the situation, taking after strategies can be utilized: boundary value analysis, comparison testing, equivalence partitioning, error guessing or now and again, graph based testing techniques. It additionally conceals testing for look-up conditions.
Some of the time, it is likewise alluded as ‘Source to Target count testing’. In this check, matching of number of records is checked. In spite of the fact that this is not the most ideal way, but rather in the event of time crunch, it makes a difference.
Here test engineer, maps data from source to target and distinguish whether the information is mapped or not. Taking after are the key checks: UNIQUE, NULL, NOT NULL, DEFAULT, CHECK, Primary Key Foreign key.
It is by and large executed in mission basic or financial project. Here, test engineer, approves every data indicate and coordinate source target data.
GUI or navigation testing:
To test the GUI or navigation aspects of front end reports.
Duplicate check testing:
It is done to guarantee that there are no copy values for one of a kind columns. Duplicate data can emerge because of any reason like missing primary key and so forth.
- To verify if all attributes of source system are there in target table.
- Historical and incremental data testing:
- Test to check the data integrity of old and new data with the expansion of new data. It additionally covers the validation of purging policy related situations. If there should be an occurrence of ETL or data warehouse testing, re-testing or relapse testing is likewise a portion of this effort. Their idea/definition continues as before.
ETL Testing is unique in relation to application testing since it requires a data driven testing approach. A portion of the difficulties in ETL Testing are:
– ETL Testing includes looking at of huge volumes of data ordinarily a large number of records.
– The information that should be tested is in heterogeneous data sources (eg. databases, flat files).
– Data is frequently changed which may require complex SQL queries for comparing the data.software testing jobs in pune
– ETL testing is particularly reliant on the accessibility of test data with various test scenarios.
In spite of the fact that there are slight varieties in the kind of tests that should be executed for every venture, above mentioned are the most widely recognized sorts of tests that should be accomplished for ETL Testing.
On the whole, ETL testing is quite a useful one for the industry at large. This was about ETL testing and its types.
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Checking data quality during ETL testing involves performing quality checks on data that is loaded in the target system. It includes the following tests −
The Number format should be same across the target system. For example, in the source system, the format of numbering the columns is x.30, but if the target is only 30, then it has to load not prefixing x. in target column number.
The Date format should be consistent in both the source and the target systems. For example, it should be same across all the records. The Standard format is: yyyy-mm-dd.
Precision value should display as expected in the target table. For example, in the source table, the value is 15.2323422, but in the target table, it should display as 15.23 or round of 15.
It involves checking the data as per the business requirement. The records that don’t meet certain criteria should be filtered out.
Example − Only those records whose date_id >=2015 and Account_Id != ‘001’ should load in the target table.
Some columns should have Null as per the requirement and possible values for that field.
Example − Termination Date column should display Null unless and until its Active status Column is “T” or “Deceased”.
Common checks like From_Date should not greater than To_Date can be done.
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ETL is ordinarily connected with Data Warehousing ventures however there in actuality any type of bulk data development from a source to an objective can be viewed as ETL. Large organizations regularly have a need to move application data starting with one source then onto the next for data migration or data integration purposes.