Testing Automation with ETL and Data warehouse: Meeting Common Objectives
Tests in Quality Assurance (QA) are performed in order to better understand the risks associated with software builds. As software systems grow more dispersed and complicated, quality assurance (QA) teams must execute a range of tests on a consistent basis. Throughout the Software Development Lifecycle (SDLC), these tests are automated, relocated to the left, and performed in real-time by the cooperating teams of developers and quality assurance professionals.
What is data warehouse testing and how does it work?
In data warehouse assessment, the developing and running detailed test cases is used to verify that data in a warehouse is accurate, dependable, and compatible with the organization’s data architecture. Organizations today need this approach due to the general rising importance placed on data analytics and the way complicated business insights are found on the basis of the premise that data is reliable. In the case of transactional databases, this is often unaffordable. The analytic work is performed by a data warehouse, which frees up the transactional database to concentrate on the transactions themselves.
Following the acceptance of data from various sources and storage in the warehouse, the process of data warehouse testing does not begin immediately. Rather, it targets the whole data pipeline, including data in-flight throughout the extraction, transform, and load (ETL) processes, as well as data in storage. It becomes possible to discover and address issue areas more rapidly when data is validated at intermediate stages of the process.
The ETL data warehousing also includes business intelligence (BI) reports and dashboards that are generated based on the consolidated data as their data source. Adding this extra layer of validation after all ETL operations are completed helps to ensure that the data is of high quality after it has been processed.
In core, data warehouse testing includes both ETL testing and business intelligence testing, which are both critical elements of any warehouse.
What is ETL Testing and how does it work?
When data is loaded from a source to a destination following a business transformation, ETL testing is performed to confirm that the data is correct. Data validation also includes the verification of data at numerous intermediate stages that are employed among the source and the endpoint. ETL is an abbreviation for Extract-Transform-Load.
Objectives for test automation in data warehouse and ETL processes
The testing (or development) company would make critical judgments about what to automate when to automate it, and whether or not it is really necessary to automate anything at all. A big part of the effectiveness of an automation project is determined by choosing the optimum product characteristics for use in automation.
It is best to avoid automating features that are in an uncertain situation or that are undergoing revisions. A full end-to-end ETL data warehousing testing solution is currently unavailable as a commercial instrument or as a collection of approaches or procedures. In the case of ETL test automation, major ETL product manufacturers do not push a specific test tool as the solution.
ETL’s performance testing is a must
There are certain ETL tests that are not appropriate for automation. Create a business case for the use of automated testing. Because automated testing is often more expensive than manual testing, IT must first establish a financial case for its use before communicating its benefits to the organization.
Examine the many alternatives. Following an evaluation of the present status and needs within the IT department, determine which tools are most appropriate for the organization’s testing procedures and environments, then implement them. Vendor-supplied tools, open-source tools, in-house tools, or a mix of tools are all possibilities.
An ETL data warehousing performance may be tested using a process called performance testing to guarantee that it can manage the load of numerous users and transactions. The major purpose of ETL Performance Testing is to optimize and increase session performance by identifying and eliminating system failures. This is accomplished via a variety of methods. It is possible that performance bottlenecks exist in the source and destination databases, mappings, sessions, and the system itself.
As test automation quickly gains traction as a viable alternative to manual testing, more businesses are searching for tools and ways to implement automation effectively. This development has resulted in a large increase in the number of test automation tools.
However, in order to make full use of these automation capabilities, the DW and BI QA teams must be equipped with the necessary programming expertise. To that, I’ll add. Businesses are becoming more nimble, and apps are being updated on a more regular basis than ever before.