ETL/data warehouse testing is a process to ensure that data flows correctly and consistently through the entire system. It’s critical that all data sources are being pulled correctly and transformed into the appropriate target system so that business users can store, process, analyze, and visualize them.
The following are the key aspects to consider when it comes to ETL/Data Warehouse Testing:
Data transformation testing
Data transformation testing is a type of data quality testing that includes data cleansing and data standardization. It is the process of removing redundant, inaccurate, or invalid data from a database. This may include correcting errors such as misspellings or incorrect dates of birth. Data standardization is organizing and structuring data so humans can easily view it. For example, if you are using MySQL as your database management system (DBMS), you can use functions like CONVERT() to convert all text fields into date types so that users can easily enter dates without guessing at formats.
Data Quality Testing
Data quality testing is the process of ensuring that the data is consistent and accurate. It involves comparing data against the business rules to identify any errors. This testing can be performed at both the load and verify phases of an ETL process.
It is important to perform these tests because it will help ensure that all your transactional processes are error-free, which improves data integrity, reduces costs associated with fixing issues, and helps you meet compliance standards set by regulators such as Sarbanes-Oxley Act (SOX).
Data quality testing should be performed during an ETL project before it goes into production or when making changes in existing systems to address any issues proactively rather than reactively when they arise later down the line.
Performance Testing is a type of software testing that verifies the performance and capacity of the application under test. Performance testing generally focuses on measuring response time, throughput, resource usage, or other factors that affect the performance of a system.
Performance tests can be used to evaluate many aspects of an application’s functioning, including:
Response times for each transaction (e.g., web pages) within an application
Transactions per second (TPS) based on user load
Throughput of transactions per second with varying workloads
Production Validation Testing
Production Validation Testing is a process used to validate the data transformation and integration between the source and target systems. It is executed in the production environment to ensure that the data flow correctly between the source and target systems.
This test aims to validate that the data transformation and integration between source and target systems have been successfully implemented. This helps ensure that business users can use the data migration project results in their daily operations without encountering any issues.
Source to Target Count Testing
This testing is done to check whether the data has passed from one system to another and is showing up in the target system as expected.
To perform this test, you must create a table in both source and target systems with similar schema and data types. Then copy records from one table to another using a simple query or BCP utility. After that, compare both tables for matching records. If there are any missing records, there is an issue with the ETL process that needs fixing at the earliest possible time. Otherwise, there may be a loss of data caused by these missing records during reporting time or for any other purpose when users start working with production data instead of the development/test environment. Reporting errors will be raised due to conflicting information.
Data & Constraint Check
Data & constraint checks are performed to ensure that data is complete, accurate, and consistent. The data is checked before it gets used by the application. It can be done manually or automatically. In an ETL process, we may have multiple tables with different columns (column A in table 1, column B in table 2, etc.). We need to check if all the required columns are available in each of them before loading them into another table.
Application Migration Testing
Application migration testing aims to ensure that the migrated application works as expected. It’s also known as compatibility testing, which is performed to check whether the migrated application works as well as its pre-migrated version.
Migration testing can be performed in two ways:
Manually (i.e., end-to-end manual tests)
Automatically (i.e., automated test tools)
Duplicate Data Checking
Duplicate data is a common problem and can be difficult to identify. Imagine you have two identical rows of data, each with the same name, address, and phone number. There are many ways to identify duplicate records—some use keys, others use sequence numbers or dates—but all have one thing in common, they need to match on some level to be considered duplicates.
The best practices for removing duplicate records vary depending on your specific requirements, but most methods fall into one of these categories:
Deletion by matching certain fields (e.g., first name)
Deletion by using an algorithm that compares every field between two records (e.g., Last Name)
A combination of both types
Data-centric testing is a type of testing where the focus is on the data in the database. The following are some of its key aspects:
Data-centric testing ensures that there is sufficient data to meet business requirements. For example, if your business requires all customer information to be stored for seven years, this must be reflected in the test cases you create.
Data-centric testing can be used to ensure that timestamps are correct and match compliance requirements (or other rules) for each record. For example, if every transaction should have an audit trail that shows when it was created or changed and by whom, then this information will need to be added as part of any relevant test cases you create for your ETL process(es).
Data-centric tests involve checking how long it takes for data from various sources to flow into the target system while considering multiple factors (for example, geographical location). This enables you to identify any bottlenecks with your pipeline infrastructure before they become major issues during production rollout.
Business testing is one of the most important aspects of data warehouse testing. Business testing is a process of validating business rules, business requirements, and business processes. Business Testing is carried out by business analysts, business users, and data warehouse developers. The main objective of Business Testing is to ensure that all the functionalities are working as expected or assumed by end-users.
Business testing can be divided into two categories:
System Testing – This testing verifies whether the required functionality has been implemented properly and whether all features work according to the user’s expectations (e.g., storing data in tables, displaying reports, etc.).
End User Testing – This type focuses on verifying if a user interface meets its needs in terms of visual appeal, ease-of-use, etc., apart from verifying if it performs all required functionalities (e.g., Exports/Imports).
Data Accuracy Testing
The first step in data accuracy testing is to define the problem. It’s easy to get sidetracked by other people’s goals. If you want to make $1,000 more per month, don’t worry about what others are doing—make sure that your goals are realistic and feasible.
It can be helpful to set a few tangible fitness goals in advance of starting any training program. For example, if your goal is to lose 10 pounds over three months, then create an action plan for accomplishing this goal (e.g., eat fewer calories than you burn each day). Similarly, if your goal is to bench press 100 pounds more than you currently lift within six months’ time period, then plan out how many sets and reps will help build up those muscles while also allowing them rest between workouts (perhaps two sets of 8 reps at 50 lbs would be most effective).
Data Completeness Testing
Data completeness testing ensures that all the records in a data store are valid and complete. The data store could be a table, view, or cube.
Data completeness is critical because it ensures no erroneous records exist in the data warehouse. It can also indicate if any of your ETL processes have failed or if you have made any mistakes when entering transactional data into your database tables, views, or cubes. Data completeness testing should include the following:
Checking whether all required fields have been populated
Counting rows/records to determine whether they meet minimum threshold requirements (e.g., 100 records per batch)
Checking for duplicate values within a column (for example, customer name)
ETL and data warehouse testing are one of the most important aspects of quality assurance. These two require a lot of attention to ensure that data is extracted from one platform and smoothly moved to another.
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