Credible data is the fuel for business processes and analytics. A 2017 Harvard Business Review study found that 47 percent of new data records have one critical error. Adhering to testing protocols ensures such errors are eliminated when the information transmission takes place from source to destination in an ETL process. ETL testing tools streamline the data journey from extraction and verification to insights, ensuring accurate results. But what is ETL testing? Let’s take a look at it in detail.
What is ETL Testing?
ETL testing validates data when it’s transferred from source to destination after transformation. The process also prevents data loss and duplication and ensures that the transfer complies with validity checks. The aim is to remove bottlenecks that may occur during data delivery. Hence, ETL testing tools will ensure that any errors or data issues are tracked and accounted for.
ETL testing can be automated to keep up with the changing business requirements, especially when testing complex ETL flows. Automated ETL testing tools simplify this task by eliminating the hassle of writing scripts and efficiently executing repeatable processes.
Why Do We Need ETL Testing?
Moving data in the ETL process can lead to numerous human or system-based errors that can negatively impact the efficiency of a business. ETL testing is needed to make sure that such errors do not occur, and if they do, a strategy is in place to eliminate them. The main strategies that ensure data accuracy are data completeness, data reconciliation, and data quality.
Types of ETL Testing
There are nine types of ETL tests, and they fall under four broad categories: new system testing, data migration testing, change testing, and report testing. However, the nine ETL testing types are listed below:
- Production validation: This type of ETL test validates data in production systems and checks it against the source data to find any faulty logic, failed operational processes, etc.
- Source to target count testing: It checks if the number of records in the target database is consistent with the expected record count.
- Source to target data testing: This ETL testing type makes sure that projected data is included in the target system without any loss.
- Metadata testing: It performs data index, type, and length checks of metadata of ETL application.
- Performance testing: It ensures that the data warehouse has the loaded data within the expected time frames and the individual response of the test server is sufficient for required performance and scalability.
- Data transformation testing runs SQL queries to check that data is accurately transformed according to the expected business rules.
- Data quality testing: It performs syntax and reference tests to ensure that the ETL application reports invalid data and accepts default values.
- Data integration testing ensures that the data from various sources have been loaded accurately to the target data warehouse.
- Report testing: It reviews data to ensure that summary report, layout, and functionality are required and performs calculations.
Addressing the challenges in the ETL pipeline early on can prevent project delays and costly overheads. And ETL testing ensures smooth execution of the flow. However, the tester should have a clear grasp of the business requirements before starting the test.
To make sure your ETL test runs smoothly, watch out for these challenges:
- Loss of data during the transformation phase
- Frequent changes in the end-user requirements
- Temporary or restricted access to source systems
- Varying formats and structures of the source data
- High data volume for testing
- Incomplete business information
- Unstable testing environment
Classifying ETL Tests – A Simple Breakdown
Broadly, ETL testing steps can be classified into the following types:
|Data Comprehensiveness||Running this data test ensures that all the required data is loaded from the source system into the destination. It requires comparing data values between the source and target system and validating the transferred information. Moreover, it checks the boundaries of every field for the data type or database column limitations to make sure data is loaded effortlessly.|
|Data Accuracy||As the name implies, this test checks for the accuracy of the transformed and loaded data. Value comparison is also an essential step in comparing data between the source and target system for accuracy.|
|Data Validation||Also known as the source to target testing, it ensures that the data is transformed as expected concerning the data format.|
|Performance||End-To-End testing verifies that all stages in the ETL process are performing within the required time frame given the data volume and complexity. The primary purpose of running this test is to determine if the ETL system can handle the expected load.|
|ETL Regression||Regression testing is performed to check the functionality of the ETL flow for a specific input before and after the change.|
|Incremental ETL||This test verifies if the incremental updates in the source are loading into the destination system as they are supposed to.|
|Metadata||Metadata testing ensures that the data integrity is retained up to the metadata level. The process involves validating the source and target table structure concerning the mapping requirements.|
Checklist for Ensuring a Successful ETL Testing Job
The testing process can be time-intensive, but following the practices above can streamline the procedure and pave the way for accurate and fast testing using efficient ETL testing tools.
Analyze the Data and Business Requirements
Evaluating the data, scope of the ETL project, and business requirements beforehand will enable you to set up an accurate testing and production environment from the get-go. Therefore, it’s better to study the type, source, format, and structure of the incoming data, in addition to the source and target schema, to get accurate test results.
Fix the Source Data
If there are any inaccuracies in the source data, identify and correct them before sending the data forward through the ETL data pipeline. Identifying errors at an initial stage reduces the risk of project failure at the execution stage.
Check for Data Integrity
The data extracted from the source(s) ultimately has to be used for analytics, reporting, or some other business task. Therefore, refine data gathered from the source(s) by applying data quality rules to filter out the redundant information and get the desired range of data.
Verify System Compatibility
Checking for data compatibility with the existing system will notify you of any variations that you may encounter when using or accessing the system. Also, make sure that all the fields that make up the table structure in the source system, such as data type, length, index, etc., are compatible with the target system.
It is also recommended to perform ETL testing on the same storage or file system(s) used at the client site to test data with real-time specifications.
Assess the Performance
Evaluate the ETL flow performance to check for faults or bugs and record them for future use. It’s best to validate access and connectivity attributes at this point to ensure smooth process execution.
Divide and Conquer
If the ETL flow is complex, it’s best to divide the roles and responsibilities in the test environment to different team members.
Documenting all the information acquired in these steps can help reduce the ETL challenges in the long term.
How to Use ETL Testing Tools Effectively – The Process
The ETL testing steps can differ according to every organization’s unique requirements; however, it can be divided into three phases:
- Plan and design: The initial step includes planning for the ETL test given the dependencies, challenges, and mitigation plans associated with the ETL process.
- Implement: Perform the test until the ETL objectives are met. This includes running and monitoring the job, error logging, error rectifications, and regression testing.
- Conclude: The last step involves preparing a summary report and concluding the test to be forwarded to the next phase, i.e., reporting or analysis.
What Are ETL Testing Tools?
ETL testing tools help check the ETL flow in a data warehouse system. SQL query testing can be used for manual ETL testing, but it is a time-consuming, tedious task with a high risk of errors. Therefore, ETL testing tools are preferred to provide automation to eliminate repetitive, manual ETL flows and offer full test coverage.
How to Find the Right ETL Testing Tools
The best ETL testing tools can reduce the burden on IT personnel and streamline the three-step process of data extraction, transformation, and loading to gain insights.
Here are some of the key features to look out for when comparing ETL testing tools:
A scalable ETL testing software will benefit you in the long term by easily accommodating changes in data volume, complexity, and variety. This is where automated solutions take preference over hand-coded ETL tools as they are comparatively easier to scale and manage. To accommodate any changes in the ETL process and associated parameters, you can modify the tool’s settings with just a few clicks instead of writing codes manually.
Responsive Support Team
Consider purchasing an ETL testing tool that has a responsive support team. Your organization will depend on that tool to test and integrate large volumes of data and possibly compare millions of records. If it comes with a responsive support team, any errors during setup or operating the ETL software will be resolved in time. In short, you’ll know your enterprise data is in good hands.
If it’s difficult to query data using the ETL testing tool, you will have difficulty testing your ETL flow. Not to mention, it will incur more time, cost, and labor to execute the tests. The alternative is to look for a tool with a graphical user interface that is easy for non-technical users.
Evaluate tools used for ETL testing based on features that are critical to your long-term integration needs. For instance, data quality and profiling can be termed as must-have features in ETL testing software. What are some other features that you may require? Automated processes which apply rules to fix any errors in the data. This is the primary function of data quality and data validation testing tools. If you’ve narrowed down a data quality testing tool that has all the must-have features on your list and meets the price points but lacks in the should-have features, you can connect to the vendor and get a sense of their roadmap to see if it will be able to meet your future ETL requirements.
Automate ETL Testing with Astera Centerprise
Automation is now a de facto standard in software development and testing. It empowers organizations to conduct comprehensive testing in less time.
Astera Centerprise is an automated, enterprise-ready ETL solution that offers testing and integration capabilities for data of any complexity, size, or format in a drag-and-drop UI. The solution has built-in connectors and transformations, providing ETL testers a unified platform for data massaging, validation, transformation, and more.
Here are some of the features in Astera Centerprise that enable fast and agile ETL testing:
Records Level Logs
The record level log transformation in Astera Centerprise shows you the status of each record processed in an ETL flow. The status updates appear as Error, Success, or Warning and can be viewed separately for each record along with additional details, such as error messages. By default, the software allows you to record up to 1000 errors; however, this number is customizable.
The data profiling feature in Astera Centerprise gives a detailed breakdown of the data in terms of structure, content, and quality. It can be applied at any step of the ETL flow to gather statistics and make the data analysis-friendly.
By applying data quality rules, you can identify custom warnings and errors in the incoming data and flag records that do not meet the required business criteria. This feature is beneficial in debugging as it captures statistical data that can be written into a destination for record-keeping and analysis.
Instant Data Preview
ETL testers can use the instant data preview feature to view any object’s output in the integration flow and identify mapping inaccuracies without executing the process. This simplifies ETL testing and gives a preview of the sample being transformed or loaded, shortening the feedback cycle and speeding up debugging.