ETL testing and data warehouse testing are slightly separate processes, but they are often considered synonymous. This is because they follow the same basic idea.
Data warehouses differ from databases, and they pose new challenges and vulnerabilities. This is where the importance of a well-structured ETL test comes into play. The process allows you to add new data in large quantities.
Let’s explore the concepts of ETL testing, data warehouse testing, and how they are achieved.
What Is ETL Testing?
ETL stands for Extract, Transform, Load. This refers to the three different steps that occur during this process. ETL is a series of events that allow for new data to be added into data warehouses in bulk. These warehouses can be vulnerable to data additions, so a carefully structured process is important.
Below is a breakdown of each step in the system:
The extraction step of the process is all about input data. Also called data staging or pre-Hadoop, extraction is all about placing components into your system by copying them over from the source.
The difficulty here is that the input data can be taken from various sources, featuring all kinds of different formats and qualities. This is where an important transformation flow works to change the data into the required form. Any non-transformable record is also flagged during this process.
Once extracted, the various components need to transform to fit into the desired format. Instead of taking single pieces of data from a database, data warehouses take in bulk amounts. This means that there are all kinds of different formats that need to be transformed to suit the end product.
This part of the process also involves quality control – making sure that each data element is without defects and can work in the format needed. This step is also referred to as MapReduce.
Once extracted and formatted, the data is then sent to the final repository. This is to say that the data loads into the data warehouse. This final step of the process takes large amounts of data to a single point where it can all be stored together.
Throughout this testing phase, each step should find potential problematic data. Going through these three separate steps will help to better refine the eventual outcome.
Data Warehouse Testing
While ETL testing is about preparing the data for the warehouse, data warehouse testing covers a broader range of stages. ETL testing makes sure that there are no bugs that enter the data warehouse – validating the data that gets transferred. Data warehouse testing involves several development phases spread throughout the data warehouse.
This includes a reporting stage, checking the data for corruption, security testing, backup recovery testing, scheduling software, and performance queries. Performing the ETL process loads the data into the warehouse where you can easily view and work with it.
Data warehouse testing is a process of checking through this data and making sure that it is compliant, and that it performs as expected.
Testing here checks the data completeness (making sure that all the data was loaded), and the data correctness (making sure that the upload was accurate). Then there is performance testing, metadata testing, syntax testing, and data validation. At the end of this, the tester will understand whether the data is correct, that it transferred correctly, and is in a safe working condition.
The Testing Process
Data warehouse testing is a process that consists of four basic steps. These steps are necessary for the complete accuracy and safety of the loaded data. Here are these four different stages of testing.
- Test Planning
This step changes depending on the specific requirements of the business and situation at hand. However, the common idea behind this is to understand the needs of the data and consider the risks, mitigations, and dependencies.
- Test Design
This is where design mapping and SQL scripts are introduced. When doing the test design, the tester will look at all of the available scenarios and queries from the inputs used.
This is the part of the process where you execute all jobs and tasks to achieve the result. This includes the ETL process, SQL scripting, testing for defects and regression, and logging these. Here, it is necessary to make sure that the software meets all exit criteria
- Test Closure
Test closure is where you end off and summarise the test. This step is about signing off the completed tasks and making sure that all results met the requirements.
ETL and Data Warehousing Challenges
Data quality issues are a common problem with many organizations. When performing data warehousing, it is necessary to execute tests properly to avoid inaccuracy and bad data. Having quality issues with the stored data can result in numerous other problems. Here are some of the challenges that you might face when undertaking 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 incorrect data in your business’s system can lead to all kinds of bad decisions that result from the information. No matter what organization or industry uses the data, it will inevitably end in mistakes and wrong results. Having your data correctly transferred and loaded is crucial, as this is the only way that you will be able to access correct, accurate data.
When shifting bulk amounts of data into a warehouse, many potential risks could occur. This is where a thorough ETL process and testing phase is necessary. Accurate data is essential for the correct operations of any business, and efficient testing is the only way to ensure this outcome.
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