Difference Between ETL Vs Database Testing
Before we even get into the testing genre of ETL or DB, let us be fully aware of what each of them essentially signifies in the real world. ETL stands for Extract, Load, and Transform. It takes care of end to end process of loading of data from the source system to the data warehouse. Now, there might be a question on what is a data warehouse? The data warehouse is that “Database” about the testing of which will be discussed in this article. This warehouse is built by the integration of data from different sources, homogenous or heterogeneous in nature, and constructed in such a way that high-quality information is retained which in the process would help in reporting requirements of all levels.
For example, there might be daily data of an interaction of an organization with its customers, employees, finances, and so on. All these data reside as different files or tables or whatever digital data from one would keep it. ETL will make sure that all these data are processed and only high-quality information is kept for further usages like Reporting, Analysis, Quality check and interpretation, and many more of Business Intelligence.
Head to Head Comparisons Between ETL Vs Database Testing (Infographics)
Below are the top comparisons between ETL and Database Testing:
Understanding ETL and Database Testing
There are few steps that are an essential part of the ETL process which we will go through as it will help us understand the differences which would come up in testing.
The first step is about Extract, which is a very simple step where relevant data is extracted from the files. Then we move to the next step of Transform. This is where a lot of operations happen on the data to make it viable high-quality data. In this step, it is first transformed into a Data Warehouse format. Then the main step of defining a key is done. This is to make sure that there should be a column that identifies the row to be unique. These keys are owned by a data warehouse and no entity can assign them. In the last part of the transform step, we cleanse the data of some unwanted errors which might have crept during transformation making it ready for loading. The last step is of Load, wherein the cleaned data is loaded into the Warehouse and corresponding aggregates are built for later BI purposes.
Key Differences Between Lightroom CC vs Lightroom Classic
Let us discuss some of the major key differences between Lightroom CC and Lightroom Classic: Now we have a fair understanding of what’s there in ETL and what’s there in Database, let us now look at some genre where differences exist and then describe them in detail.
- The first genre is of the intention behind the testing of each. ETL testing is intended to have an impact on the BI reporting and all the steps of ETL (Extract, Load and Transform) need to be tested so that the data is ready for BI reporting. In database testing, the testing is done on validating and integrating the data. This is to check if there are valid values in the column of the database. For example, a date column can’t have any invalid data like a string value, or a date from the 1400s.
- Now that we know of the intention behind each testing, one should be aware of its importance in the business. Of course, without importance the survival of the process is difficult in a business flow which tries to optimize the days to turn around a minimum viable product into the market. Database testing helps in keeping a check on the integration of data from different sources into one place, whereas on the other hand ETL testing is more to test out the data we have extracted is viable and important for BI reporting.
- The third genre is understanding which systems are applicable for each type of testing. ETL testing is applicable for those systems where historical data is present and not necessarily a business flow environment and on contrary, ETL testing needs to be performed on a system that has an active transactional system where there is business flow.
- The next genre is about the model which is used for testing the different workflows, ETL, and Database. For ETL testing we use a multidimensional model for testing because the data has sourced from multiple sources and perspectives. This is to make sure that we enable the capability of complex analysis and ad hoc queries. And for Database testing, we use an ER (Entity Relationship) model where data elements and their relationships are defined.
- Next, is about the database type we would be testing in these 2 types and they are, OLAP (Online Analytical Processing system) for ETL testing as OLAP platform is capable of storing historical data from OLTP (Online Transaction Processing system), which is the database type tested in Database testing. OTLP is a system that helps in a quick update, insert and delete while the process of transaction is still active.
- The data we handle for ETL testing is more of denormalized data with more of indexing and aggregation rather than joins whereas in Database testing the data has a lot of joins because they come from a wide variety of sources.
- In the end, the common tools for ETL testing which help in automation are, QuerySurge, Informatica and for Database testing, Selenium, QTP is widely used.
Comparison Table of ETL Vs Database Testing
Let’s look at the top comparisons between ETL Vs Database Testing.
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| Genre | ETL Testing | Database Testing |
| Intention | Tested with an intent to have better BI reporting | Tested with an intent to have proper data in columns |
| Importance in Business | If the data is viable and important from BI perspective | If data is integrated properly as they come from a wide variety of sources |
| Systems applicable | Systems with historical data | Systems with more of transactional data |
| Model used for testing | Multidimensional model | Entity-Relationship model |
| Database type | OLAP database, as it consists of a mostly historical type of data | OLTP database as it comprises mostly transactional data. |
| Data type | Denormalized data with a lot of indexes and aggregation | Normalized data consisting of a lot of joins. |
| Examples of tools | QuerySurge, Informatica | Selenium, QTP |
Conclusion
In a nutshell, this article gives you an in-depth understanding of what basic differences to look at while deciding the testing you would need to flow in your professional work and plan your testing architecture in such a way that essential components don’t get missed during the flow, else in production critical scenarios might come in and lead to a loss in the lifecycle of the software.
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