Automate your ETL testing for faster and accurate outcomes

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Extract, Transform, Load (ETL) has become a common procedure in the software world since its introduction into the realm of data warehousing and business intelligence.  

An ETL process, as the name implies, consists of three separate steps that frequently occur in parallel: data is extracted from one or more data sources; it is converted into the appropriate state; and it is loaded into the desired target, which is typically a data warehouse, mart, or database.  

Error-handling, logging infrastructure, and the routine environment are frequently included in an ETL procedure. 

ETL tools have traditionally been used to prepare massive, disparate data for analytics and business intelligence.  

However, its applications are expanding beyond merely transporting data, with data migration for new systems, as well as data integrations, sorts, and joins, becoming more popular. 

ETL is thus an essential component of today’s fast-paced development lifecycle, in which numerous releases and versions are being worked on simultaneously at any given time.  

Organizations must be able to improve, integrate, and innovate their software on a continuous basis, with data available to testers and developers in the appropriate state for each iteration and release.  

The data will come from the same places, but it will need to be altered to fit the needs of each team. 

This is especially true if an organization is attempting to be “Agile” or execute Continuous Delivery successfully. 

Despite the rising use and relevance of ETL testing, it still mirrors the condition of testing in general, in that it is excessively slow and manual, allowing an unacceptable number of problems to pass through to production.  

The Typical Approach to ETL Testing and the Common Challenges Encountered 

Testers often generate a shadow code set, utilize it to transform data, and then compare the actual results to the predicted outcomes when testing ETL transformation rules.  

ETL scripts or SQL are often copied to the source data and performed manually, with the results recorded. After that, the identical script is copied to the target data and the results are recorded.  

The actual and expected outcomes are then compared to ensure that the data has been transformed correctly. 

The underlying problem with manual validation is that ETL processes, by their very nature, grow extremely complex very quickly.  

As the company expands and the variety and volume of data it collects expands, the ETL rules must expand to keep up.  

This expansion is growing faster than traditional testing methods can handle in the so-called “information age.”  

In fact, the amount of data collected by data-driven enterprises has increased so rapidly that 90 percent of the data in the globe was acquired in the last two years alone, with the average organization’s data collection tripling every year. 

According to Ted Friedman, Distinguished VP Analyst, Gartner, “Data and analytics leaders need to understand the business priorities and challenges of their organization. Only then will they be in the right position to create compelling business cases that connect data quality improvement with key business priorities.” 

With each additional decision, the complexity of the systems built to collect, convey, operate on, and present this data expands dramatically.  

This comprises ETL rules, and there are a number of elements that can influence the transformation’s complexity. 

The testability of ETL processes is directly impacted by this increased complexity.  

It’s especially difficult for ETL testing because the transformation rules are often stored in shoddy documentation with no stated intended outcomes.  

The rules are usually created during the development phase and stored in written papers or spreadsheets – or, even worse, they may not exist beyond the imaginations of the developers and testers.  

There is no true documentation from which test cases (i.e., shadow code) may be securely derived in this scenario. 

Testers were frequently left to fill in the blanks, and when they did so improperly, defects in the ETL scripts were introduced. 

According to Gartner, “Poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits.”  

Invalid data was copied to the destination despite the fact that the code and test cases matched a plausible reading of the requirements description. 

It’s vital to have enough “poor” data for effective ETL testing, because it’s critical that an ETL rule rejects this data and sends it to the appropriate user in the appropriate format when it’s in use.  

If it is not rejected, the bad data will most likely result in flaws or possibly system failure. 

The availability of data is another key challenge for ETL validation. An organization’s source data could come from several distinct places.  

The issue with ETL testing, and testing in general, is that it is perceived as a series of linear stages, forcing test teams to wait for data while another team uses it. 

The viable alternative to effectively overcome these challenges is by fully automating ETL Testing. 

Fully Automated ETL Testing is the feasible substitute to overcome these challenges 

Any firm aiming for Continuous Delivery of high-quality software must incorporate a higher degree of automation into ETL testing.  

ETL validation still requires a significant amount of manual effort, from manually developing ghost code from static requirements to sourcing the required data and comparing the results.  

Model-Based Testing and intelligent Test Data Management may be utilized to automate each of these activities while allowing several teams to work simultaneously from the same data sources. 

Model-Based Testing “shifts left” the ETL testing effort, focusing the most of the work in the design phase.  

Every asset required for ETL testing can therefore be generated automatically in a fraction of the time. 

Automatically produced test cases with 100 percent functional coverage can be linked to separately defined expected outputs.  

Each test is then “Matched” to the specific source data required to run it, which, if saved in the Test Data Warehouse, can be distributed to multiple teams at the same time. 

The tests needed to re-test an ETL procedure may be quickly conducted after a change is made due to the close link created between tests, data, and requirements.  

The time saved by not having to manually construct, update, and re-run tests quickly outweighs the effort spent creating the initial model.  

Model-based generation also provides a significant benefit of reusability, since test components can be kept as sharable assets in the Test Data Warehouse, linked to data and expected results.  

The greater the library grows as more tests are run, until testing new or updated ETL rules is as simple as selecting from existing components. 

ETL testing is no longer a bottleneck in the delivery of applications, and it can keep up with the growth of data-driven organizations.  

The testability of increasingly complicated routines is maintained, ensuring that testing can handle the diversity and volume of data collected and does not obstruct Continuous Delivery of high-quality applications. 

Closing thoughts 

Extracting data from a variety of sources, converting it into a readable format, and uploading it to a data warehouse is a massive task that is vital to a company’s competitiveness.  

Having access to the appropriate data and analysis may make a huge difference in how businesses make critical decisions that affect their success. process 

The extraction, processing, and loading process presents a number of snags, making it a difficult task. To guarantee that ETL accomplishes its goal, it must take place in a smooth manner. This is where data warehouse testing or ETL testing comes into play. 

For our clients, Cigniti’s ETL/Data warehouse testing teams have delivered effective outcomes, including a shorter test cycle, 0% production defects, and a speedier time to market. 100+ skilled & qualified test specialists, custom test and process templates, in-house tools, framework, accelerators, and CoE round up our comprehensive big data testing services 

Connect with us to find out more about automating your ETL testing for faster and accurate outcomes. 


  • Cigniti Technologies

    Cigniti is the world’s leading AI & IP-led Digital Assurance and Digital Engineering services company with offices in India, the USA, Canada, the UK, the UAE, Australia, South Africa, the Czech Republic, and Singapore. We help companies accelerate their digital transformation journey across various stages of digital adoption and help them achieve market leadership.

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