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 at least 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 to verification to insights, ensuring accurate results.
ETL Testing — What Is It?
ETL testing validates data when 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.
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.
Challenges Associated with ETL Testing
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 tests can be classified into the following types:
Data Comprehensiveness
Running this 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 information being transferred. 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 this process that compares data between the source and target system for accuracy.
Data Validation
Also known as the source to target testing v, it ensures that the data is transformed as expected with respect to the data format.
Performance
End-To-End testing that 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 find out 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 up to the metadata level is retained. The process involves validating the structure of the source and target table with respect to the mapping requirements.
Checklist for Ensuring a Successful ETL Test
The testing process can be time-intensive, but following the afore-mentioned practices can streamline the procedure, and pave the way for accurate and fast testing.
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 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 compatibility of data 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, so the testing is done 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 process 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 so the project can be forwarded to the next phasee. reporting or analysis.
ETL Testing Tools — How to Find the Right One
ETL testing tools 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:
Scalability
A scalable tool 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 be depending on that tool to test and integrate large volumes of data, and possibly to compare millions of records. If it comes with a responsive support team, any errors that may arise during setup or operating the tool will be resolved in time. In short, you’ll know your enterprise data is in good hands.
Data Usability
If it’s difficult to query data using the tool, you will have a hard time 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-to-use for non-technical users.
Essential Requirements
Evaluate tools based on features that are critical to your long-term integration needs. For instance, data quality and profiling can be termed as a must-have feature in an ETL testing tool. What are some other features that you may absolutely require? If you’ve narrowed down a 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 the software development and testing industry. 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 is equipped with 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.
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Data Profiling
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.
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Data Quality
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 especially helpful in debugging as it captures statistical data that can be written into a destination for record-keeping and analysis.
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Instant Data Preview
Using the instant data preview feature, ETL testers can view the output of any object 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.
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Experience first-hand how Astera Centerprise can simplify ETL testing and help your organization develop confidence in the integrity of data by downloading a version.
Originally published at https://www.astera.com on September 27, 2019.
