A data warehouse is a repository or central place for storing the data from heterogeneous sources. It keeps historical and present data of an enterprise at one place, enabling them to wisely take business decisions. The movement of the bulk data from any source to the data warehouse is called ETL. ETL stands for Extract-Transform-Load. To assure that the data transferred from the source to target is accurate, testers perform ETL testing. This article is a complete ETL testing tutorial, helping individuals who are a novice in the testing domain.
Table of Contents
What is ETL?
As mentioned above, ETL implies Extract-Transform-Load. ETL is a procedure of extracting data from heterogeneous sources, transforming it using several processes, like calculation, concatenation, etc., and then loading it into the data warehouse. Adopting ETL in organizations offers numerous benefits. It helps organizations make more complex and critical business decisions and provide solutions to multiple business queries and enhance productivity.
ETL Process
The ETL process consists of three different steps – extraction, transformation, and loading.
- Extraction:
ETL tool helps users extract data from several sources and store it in the staging area. In the staging area, the transformation of the extracted data is done if required. The loading of corrupted data from the source to the data warehouse results in the need for rollback. Hence, it can be quite challenging to rollback the data from the data warehouse.
- Transformation:
The extracted data is, however raw and is not useful. Hence, there is a need for the extracted data to transform it into meaningful and usable data. Transformation of the raw data may include cleaning, mapping, and transforming. Transformed data is more beneficial for generating business intelligence (BI) reports. Additionally, the transformation of data comprises of using calculations, joins, concatenations, etc.
- Loading:
Loading the data is the final of the ETL process. The transformed data is further transferred into the data warehouse system. Once the data loads into the data warehouse system, organizations can use Business Intelligence (BI) tools to generate analytical and detailed reports required for business operations.
What is ETL Testing?
Testers perform ETL testing on the data before it is moved into the data warehouse system. This testing is also called as production reconciliation or table balancing. This testing’s primary goal is to find and reduce the data bugs and common defects before it is processed into the data warehouse system. Additionally, this testing ensures that the data that is transferred from source to target is correct and faultless.
ETL Testing Process
With the table balancing testing, testers can find out defects or problems associated with the source data before it gets transferred into the target system. This testing process involves eight different stages, as follows:
1. Identifying Business Requirements:
Firstly, testers need to identify business requirements. They feel comfortable to perform testing if the aim or goal is precisely defined. This step involves designing the data model, defining the business flow, and evaluating the report depending on the client’s requirements.
2. Validating Data Sources:
Validating data sources implies carrying out data count check and verifying that the designed data model wholly fits the column and table data type. The table should contain unique and non-redundant data, and all check keys should be in place.
3. Designing Test Cases:
The third step involves creating ETL mapping, defining transformational data rules, like concatenations, joins, aggregation, etc., and developing SQL scripts. There is a need for validating the ETL mapping document, as it stores all necessary information.
4. Extracting Data:
Once you are clear with business requirements, perform ETL tests accordingly. During the testing, make sure you build a report containing all defects and bugs you encountered. Further, work on those defects or bugs to reduce them before moving towards the next step.
5. Applying Transformational Rules:
The extracted and error-free data should match the data present in the data warehouse system. For the matching purpose, consider data alignment, data threshold, and data validation parameters.
6. Loading Data Into The Target System:
You have to apply all transformation rules on the source data in the staging environment. Therefore, you have to execute the record count check pre and post the data is moved from the staging area to the target system. When the information moves into the data warehouse system, there should be no invalid and violating data.
7. Data Report:
Data report plays a vital role for organizations in the decision-making process. It helps stakeholders know the test results in detail.
8. Test Closure:
Finally, the test closure contains detailed information about all tests performed. Additionally, it also helps stakeholders know where bugs occurred and how they were handled during the test.
Moreover, a tester needs to perform the number of tasks while performing this testing.
- Acknowledging the data used for generating reports.
- Reviewing the data model.
- Mapping data from source to target.
- Performing data checks on the source data.
- Verifying data in the target system.
- Validating schema and packages.
- Verifying the data transformation rules, like calculations, aggregations, etc.
- Comparing data samples between the source and target system.
- Checking data integrity and data quality in the target system.
- Carrying out performance testing on the data.
ETL testing tools, like QuerySurge, BiG EVAL, Informatica, QualiDI, etc., are available that make testing more manageable and accessible.
ETL Testing Types
There are several types of ETL testing. Let us go through each of its kinds.
1. Production Validation Testing:
Testers perform the production validation testing on the data that is transferred into production systems. The data in the production systems should be accurate and valid, as it helps organizations make effective business decisions.
2. Source To Target Testing:
The source to target testing ensures that the data moved from the source to target have the same and expected values.
3. Application Upgrades:
The application upgrades test assures that the data retrieved from the older repository is exactly the same as the data present in the current data warehouse system. Such testing types generate automatically, saving the test development time.
4. Metadata Testing:
This testing involves carrying out data type check, index or constraint check, and data length check.
5. Data Completeness Testing:
Testers execute the data completeness testing to ensure that all the expected data is transferred into the target system. The data completeness test involves running and comparing aggregates and valid counts of source and target data.
6. Data Accuracy Testing:
The data accuracy testing validates that all the data in the target system is loaded accurately, as expected.
7. Data Transformation Testing:
This testing involves writing the number of SQL queries and executing them to check the transformation rules. Running one or two SQL queries is not sufficient.
8. Data Quality Testing:
The data quality testing involves syntax tests and reference tests. In business operations, having an accurate date and order number is essential. Hence, this testing helps businesses to have a precise date and order numbers. The syntax test involves checking the data for character pattern, inappropriate upper or lower case order, dirty data, etc. On the other hand, the reference test includes checking whether the data matches the data model.
9. Incremental ETL Testing:
To test the integrity of the old and new data, testers perform incremental ETL testing. This testing assures that the execution of any operation, like insertion or updation, is as expected.
10. GUI or Navigation Testing:
As its name suggests, this testing ensures that all the aspects of the report’s GUI or navigation are flexible.
Responsibilities of ETL Testers
There is an immense scope of ETL testers these days and in future, as well. Therefore, IT professionals having a keen interest in the data mining domain can go for ETL testing jobs. Below are the detailed responsibilities of ETL tester while executing ETL Testing.
- Testing ETL software.
- Testing ETL data warehouse system’s components.
- Executing data-driven tests.
- Designing, creating, and running test plans, test cases, and test harness.
- Detecting issues and respective offering solutions.
- Approving design specifications and requirements.
- Writing multiple SQL queries for several scenarios.
Challenges in ETL Testing
ETL testers face multiple challenges while carrying out the testing process. Below are some significant challenges:
- Duplicate data and incompatible data.
- Massive data loss.
- Inclusive testbeds are not available.
- Extremely high volume and complexity of data.
- Faults and errors in business operations.
- Experiencing a lot of trouble while extracting and developing the test data.
- Unavailability of business flow data.
Merits And Demerits Of ETL Testing
Merits:
- It allows retrieving any amount of data from several sources at the same time.
- Organizations can load the data extracted from several sources to a single or different target system simultaneously.
- It enables organizations to transform the data as per their needs and requirements.
- This testing makes it possible to load the data from different sources into the target system in various formats.
Demerits:
- Users must be a data analyst or user-oriented developer to perform ETL testing.
- It is a time-consuming process and requires several months to put on any place.
- As the organization’s environment and needs change, it becomes difficult to maintain the data.
FAQ
- How to learn ETL Testing?
You can learn this testing online with a notable tutoring platform. You must possess the knowledge of ETL tools, SQL, scripting language, debugging, and, of course, data warehouse.