Conquering the Challenges of Data Warehouse ETL TestingCigniti Technologies
Listen on the go!
ETL stands for Extract-Transform-Load and is a typical process of loading data from a source system to the actual data warehouse and other data integration projects. It is essential to know that independent data verification and validation is gaining huge market potential. Many organizations and companies are considering implementing ETL and Data warehouse processes as they realize that valid data in production is critical for making informed business decisions.
Importance of Data Warehouse for Organizations
Organizations with well–defined IT practices are at an innovative stage, leading the next level of technology transformation by constructing their own data warehouse to store and monitor real-time data. However, such organizations realize that testing the data is business-critical as it ensures the data collected is complete, accurate, and valid. They also understand that comprehensive data testing at every point throughout the ETL process is essential and inevitable, as more of this data is being collected and used for strategic decision-making that impacts their business forecasting capabilities. However, specific strategies are time-consuming, resource-intensive, and inefficient. A well-planned and effective ETL testing scope guarantees smooth project conversion to the final production phase. Let us see some common issues with ETL and Data Warehouse testing.
Some of the challenges in ETL testing are:
- Unavailability of inclusive test bed at times
- Lack of proper flow of business information
- Loss of data might happen during the ETL process
- Existence of several ambiguous software requirements
- Existence of apparent trouble in acquiring and building test data
- Production sample data is not an accurate representation of all possible business processes
Some of the challenges in data warehouse testing are:
- Data Warehouse/ETL testing requires SQL programming. As most of the testers usually have limited SQL coding skills, it makes data testing very difficult
- Performing Data completeness checks for the transformed columns is tricky
- Certain testing strategies used are time-consuming
Types of ETL Testing
Data is essential for all businesses to make critical decisions. ETL testing plays a significant role in verifying, validating, and ensuring that the business information is exact, consistent, and reliable. ETL Testing is data–centric testing, which involves comparing large volumes of data across heterogeneous data sources. This data–centric testing helps achieve high–quality data by getting the erroneous processes fixed quickly and effectively.
Data-centric Testing: Data-centric testing revolves around testing the quality of data. Data-centric testing aims to ensure that valid and correct data is in the system. It ensures that proper ETL processes are applied on the source database, during transformation, and on load data in the target database. It further provides that good system migration and upgrades are performed.
Data accuracy testing ensures that the data is accurately transformed and loaded as expected. Through this testing, we can identify errors obtained due to truncation of characters, improper mapping of columns, implementation errors in logic, etc.
Data completeness testing: These tests help to verify that all the expected data is loaded in the target from the source. It helps to verify the count of rows in the driving table matches the counts in the target table.
Data integrity testing: This type of testing helps to check for counts of ‘unspecified’ or ‘unmatched’ rows concerning foreign keys and compares the percentage of foreign key matches by running queries on data.
Business testing: This testing ensures that the data fulfills the critical business requirements. The data is evaluated against the business rules stated. This test also checks whether data has been moved, copied, or loaded entirely and accurately.
Data transformation testing is done in many cases as it cannot be achieved by writing on source SQL query and comparing the output to the target.
Production validation testing: This testing is done on data being moved to production. To achieve effective business decisions, data in the production systems should have valid and correct order.
With the constantly evolving needs of businesses and similar changes in the source systems, ETL testing effectively drives continuous change in the data warehouse schema and the data being loaded. Hence, development and testing processes must be clearly defined. ETL and Data Warehouse testing should be followed by impact analysis, focusing on solid alignment between development, operations, and business teams. Below are some of the ETL testing tools that can be used.
ETL testing tools
ETL testing can be performed manually or using tools like Informatica, QuerySurge, etc. However, much of the ETL testing is done by SQL scripting or eyeballing data on spreadsheets. Automated testing tools ensure that only trusted data will be delivered to your production system. The types of testing that can be achieved with ETL tools include unit, functional, regression, continuous integration, operational monitoring, and more.
The benefits include reducing testing time by about 50% to 90% while reducing resource utilization. ETL testing lowers business risks and instills confidence in the data. The two documents that an ETL tester will use are ETL mapping sheets and the DB Schema of source and target.
To sum up
Extracting data from disparate sources, transforming the obtained data into a legible format, and uploading it into the data warehouse is as huge a task as it is critical for a business’s competitiveness. Having the correct data and analysis at your disposal can make a world of difference in how companies make important decisions that impact their growth. The extraction, transformation, and loading process has several challenges, making it a tricky activity. It needs to happen seamlessly to ensure that ETL serves the intended purpose. This is where ETL testing or data warehouse testing comes into the picture.
These ETL challenges and solutions underscore the critical importance of a well-structured and adaptable data pipeline, enabling organizations to efficiently extract, transform, and load data while overcoming obstacles such as scalability constraints, data quality concerns, and evolving schema requirements.
Cigniti’s ETL/Data warehouse testing teams have produced effective results for our clients, including reduced test cycles, zero production defects, and faster go-to-market. Our comprehensive extensive is complemented by 100+ expert & certified test professionals, custom test and process templates, in-house tools, frameworks, accelerators, and CoE. Connect with us today.