Software Testing delves into multiple facets, including functional and non-functional tests. Verifying that the developed application performs as anticipated typifies functional testing, while assessing aspects such as efficiency and performance constitutes non-functional testing.
A distinct type of testing in this realm is DB Testing where data is properly structured within a database forming tables. Businesses commonly have to unite or manage data from various tables into one, or vice versa.
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DB testing holds a unique purpose compared to ETL Testing. Its main role involves assimilating data from disparate sources to create reports that aid in analyzing business requirements and understanding the dynamics of supply and demand.
Conversely, ETL Testing is a significant testing strategy used in business settings when clients necessitate thorough reporting to decipher demands, needs, and supply to enhance end-user experiences.
Reading this guide, you will acquire:
- Insight into the differences between DB Testing and ETL Testing
- Comprehensive knowledge regarding ETL Testing, its needs, procedures, and planning through real-world examples
Additional details about ETL Testing can be found on our page on ETL Testing Techniques and Best Practices.
Table of Contents:
DB Testing vs. ETL Testing
While DB Testing and ETL Testing can be mistaken as similar, they possess unique differences.
DB Testing primarily finds applications in business processes that necessitate the combination of data from multiple sources into a solitary table. The data sources can include tables, flat files, applications, or any other mediums that yield output data.
DB Testing requires a thorough understanding of how data transforms from its origin to its ultimate location. It is also essential to uncover and remedy issues pertaining to data or DB synchronization. Addressing these concerns aids in reducing potential invalid defects or bugs.
Project “A” leverages data from diverse sources. In such a case, maintaining data integrity within the destination location is paramount, which involves validating factors like primary foreign key, column value integrity, and null values within the columns.
ETL Testing is particularly used for the purpose of reporting. Clients often depend on these reports to understand customer behavior based on parameters such as product preferences and purchase dates.
ETL Testing involves data warehousing where historical business data is stored. This type of testing examines multiple levels of data transformation and verifies data completeness and accuracy.
A retail business collects customer data and assigns a unique membership ID to every customer. After a certain duration, they examine the customers’ buying habits, adjust their membership statuses based on their usage, and develop reports identifying customers who meet specific conditions.
DB Testing is principally focused on data integration, while ETL Testing primarily concerns itself with business intelligence (BI) reporting.
The following is a snapshot comparison between DB Testing and ETL Testing:
|Within the functional system where business flows transpire
|Exterior to the business flow surroundings. Inputs are drawn from historical business data
|Informatica, QuerySurge, COGNOS
|Impact on business
|Can result in serious impacts due to integrated architecture
|Potential impacts when clients need forecasting and analytics
|Online Transaction Processing
|Online Analytical Processing
Why Businesses Should Opt for ETL?
Companies have distinct requirements and life cycles for their products. By examining historical data through ETL Testing and generating reports, companies can comprehend customer demands and customize their offerings accordingly.
Below are some reasons why companies should think about ETL Testing:
- Identification of data and tables: It’s crucial to select pertinent data and tables for ETL Testing to focus on forecast and analysis of customer needs.
- Mapping sheet: A mapping sheet is a fundamental requirement in ETL Testing. Any anomalies or incorrect mapping data can affect the entire ETL deliverable.
- Table designs, data, and column type: It’s critical to ensure compatibility between column types and data in user and destination tables for successful ETL Testing completion.
- Database access: Access to the database employed for ETL Testing is essential. Restrictions on database access can impede the testing process.
ETL Reporting and Testing focus on the analysis of historical data for strategic decision-making.
A silk fabric production company aims to analyze the sales over a year. Using ETL reports, they noticed a considerable drop in sales during certain months. In response, they introduced discounts and promotions, which subsequently increased their sales.
Planning for ETL Test
Proper planning is a prerequisite for successful ETL Testing. This includes defining requirements, mapping data, and developing test cases.
Mapping sheets are integral to ETL Test Planning. They establish relationships among different databases and define the transformations and logic applied to the data.
As an example, a mapping sheet may house VIEW_NAME, COLUMN_NAME, and DATA_TYPE columns to define views for extracting data from the source and tables to store data in the ETL landscape. Adhering to a naming convention can simplify automation planning.
Identifying crucial data and tables from the source together with mapping the tables for ETL are vital steps.
Here’s an illustrative example:
ETL Test Planning comprises:
- Identifying pertinent data and tables
- Forming a logical data mapping sheet
- Familiarizing and examining data sources
- Managing initial and incremental data loads
- Accounting for audit columns
- Loading facts and dimensions
Key ETL Requirements
ETL Testing caters to specific business needs such as forecasting, reporting, and analysis for effective capture and comprehension of customer requirements.
Following are some pertinent ETL needs:
- Data and table identification: Selecting relevant data and tables for analysis helps avoid redundant or irrelevant data that may impede precise forecasting and analysis.
- Mapping sheet: Mapping tables from the source to the destination is essential. Any issues or faulty information in the mapping sheet can affect the entire ETL process.
- Table designs, data, column type: Ensuring compatibility between the column types and data in source and destination tables is critical.
- Database access: Uninterrupted access to the ETL database is crucial for smooth testing.
ETL Reporting and Testing
ETL Reporting is essential for examining historical data and comprehending customer needs. For instance, an airline company might utilize ETL reports to identify customers journeying between region A and region B on specific dates to design appropriate promotions.
Contrarily, the goal of DB Testing is to uncover and resolve issues related to invalid or incorrect data within databases.
Common Challenges in ETL Testing
ETL Testing encounters specific challenges that need to be tackled to ensure accurate data transformation and reporting.
Below are some frequent issues:
- Invalid or outdated extraction criteria that result in no data within the table for ETL Testing
- Inconsistent column names and data types between source and destination tables
- Differences in record count between source and destination tables
Mapping Sheets hold a pivotal role in ETL Testing and provide a lucid understanding of transformation logic and data interrelationships.
Considerations during ETL Test Planning and Implementation
Proper planning and execution are indispensable to productive ETL Testing. It is critical to ensure correct data loading based on extraction criteria to alleviate potential issues.
A few salient points to consider:
#1) Data extraction from heterogeneous sources
#2) Managing ETL operations in an integrated setting consisting of diverse DBMS, OS, hardware, and communication protocols
#3) Creating a logical data mapping sheet before transforming physical data
#4) Familiarizing and examining data sources
#5) Overseeing initial and incremental data loads
#6) Accounting for audit columns
#7) Loading facts and dimensions
Noteworthy Uses of ETL Tools
ETL tools primarily aid in constructing and converting transformation logic, mapping schemas, cleaning data, and loading data efficiently at the destination.
The utilization of ETL tools significantly reduces manual labor by automating mapping and verification processes.
Renowned ETL tools comprise of:
- Informatica – PowerCenter: This ETL tool by Informatica Corporation is widely popular. It offers client tools, repository tools, and servers. More information is available here.
- IBM – Infosphere Information Server: Developed by IBM, it provides comprehensive information integration and management. More details are available here.
- Oracle – Data Integrator: This ETL tool developed by Oracle Corporation comes in various versions catering to diverse customer needs. Visit their site here for deeper insights.
ETL Testing and reporting enable businesses to inspect historical data to recognize customer needs and improve their offerings yielding mutual benefits.
About the author: Nagarajan, with over 6 years of experience in Software Testing across functional domains including Banking, Airlines, and Telecom, is currently a Test Lead. His expertise spans both manual and automated testing.
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