Although ETL testing and data warehouse testing are slightly different processes, they are sometimes confused. This is due to the fact that they are based on the same core concept.
Data warehouses are not the same as databases, and they provide unique difficulties and risks. This is where the value of a well-structured ETL test becomes apparent. The procedure enables you to add massive amounts of new data.
Let’s look at what ETL testing and data warehouse testing are and how they’re done.
What Is ETL Testing?
Extract, Transform, and Load is an acronym meaning Extract, Transform, and Load. This refers to the three steps that take place during the process. ETL is a set of processes that allow new data to be loaded into data warehouses in large batches. Because these warehouses are susceptible to data additions, a well-structured approach is essential.
Below is a breakdown of each step in the system:
The input data is what the extraction step of the procedure is all about. Extraction, also known as data staging or pre-Hadoop, is the process of moving components from the source into your system.
The problem is that the input data can come from a variety of places and in a variety of formats and quality levels. This is when a crucial transformation flow comes into play to turn the data into the desired format. During this process, any non-transformable records are also marked.
The individual components must be transformed to fit into the correct format once they have been retrieved. Instead, then extracting individual records from a database, data warehouses import large volumes of data. This means that a variety of formats must be converted to fit the final product.
This step also includes quality control, which ensures that each data element is free of errors and may be used in the format required. MapReduce is another name for this stage.
The data is then delivered to the final repository after being extracted and prepared. That is, the data is loaded into the data warehouse. The process’s final stage gathers enormous amounts of data into a single location where it can be stored collectively.
Each step in this testing phase should uncover potentially problematic data. Going through these three distinct phases will assist in fine-tuning the final result.
Data Warehouse Testing
While ETL testing focuses on preparing data for the warehouse, data warehouse testing encompasses a wider variety of activities. ETL testing ensures that no flaws make their way into the data warehouse, validating the information that is transferred. Several development phases are scattered throughout the data warehouse during data warehouse testing.
This stage contains a reporting stage, data corruption checks, security testing, backup recovery testing, software scheduling, and performance queries. The data is loaded into the warehouse via the ETL process, where it can be conveniently viewed and worked with.
Data warehouse testing is the process of examining this data to ensure that it is accurate and compliant, as well as that it functions as planned.
The data completeness (ensuring that all of the data was loaded) and data correctness are checked here (making sure that the upload was accurate). Then there’s data validation, performance testing, metadata testing, and metadata testing. The tester will know whether the data is correct, that it was transferred correctly, and that it is in a safe operating condition at the end of this.
The Testing Process
The testing of a data warehouse consists of four fundamental processes. These steps are required to ensure that the loaded data is completely accurate and secure. These are the four separate testing steps.
1. Test Planning
This step varies depending on the business’s specific needs and the current condition. However, the general concept is to comprehend the data’s requirements while also taking into account the risks, mitigations, and dependencies.
2. Test Design
This is where you’ll learn about design mapping and SQL scripts. When designing the test, the tester will consider all of the possible scenarios and queries based on the inputs.
This is the part of the process where you carry out all of the duties and tasks necessary to attain the desired outcome. The ETL process, SQL scripting, defect and regression testing, and logging are all part of this. It is vital to ensure that the software satisfies all exit conditions in this case.
4. Test Closure
When you finish a test, you should wrap it up and summaries it. This phase included signing off on completed work and ensuring that all outcomes complied with the requirements.
ETL and Data Warehousing Challenges
Data quality challenges are a typical occurrence in many businesses. To eliminate inaccuracies and faulty data, adequate test execution is required when implementing data warehousing. Having problems with the stored data’s quality can lead to a slew of other concerns. Here are some of the difficulties you may encounter when performing ETL and data warehousing.
- A good record can be cut short
- It is also possible for a good record to be skipped out or not loaded into the warehouse
- Records may also write over multiple times, providing an inaccurate collection of data into the target destination
- Records may change incorrectly during the transformation stage. This will result in problematic or inaccurate data loading into the warehouse
Having erroneous data in your company’s system might lead to a variety of poor judgments. Whatever institution or industry uses the data, it will always result in errors and incorrect outcomes. It’s critical that your data be correctly transferred and loaded because that’s the only way you’ll be able to obtain correct, accurate info.
Many hazards can arise when transferring large amounts of data into a warehouse. This is where a comprehensive ETL procedure and testing phase are required. Accurate data is critical for a company’s proper operations, and effective testing is the only method to achieve this.
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Also Read: https://www.guru99.com/software-testing.html