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Iddo Avneri
Iddo Avneri Author

Iddo has a strong software development background. He started his...

Last updated on April 29, 2024

Imagine spending significant time and money migrating your data to a new system only to discover that the data is partial or incorrect. Not only would this result in costly errors, but it might also lead to poor business choices based on inaccurate data. This is a common issue teams face throughout the Extract, Transform, and Load (ETL) process.

ETL testing is a step you can’t afford to skip if you’re looking to integrate and migrate your data to a new system. It works like a safety net for your data, ensuring completeness, correctness, and dependability to boost your decision-making capabilities.

ETL testing may be challenging due to the volume of data involved. Furthermore, the data is nearly always diverse, adding another level of complexity. However, it doesn’t have to be that way. With the right process and tooling, even new team members can quickly get started with ETL testing.

This article covers everything you need to know about ETL testing. Keep reading to explore ETL testing best practices and dramatically improve data quality within your organization.

What is ETL (Extract, Transform, Load)?

Extract Transform Load (ETL) is about gathering vast amounts of data from many sources, changing and rearranging it for reporting and analytics, and then putting it into a data warehouse. 

What is ETL Testing?

ETL testing determines whether the ETL process is working correctly. It’s an essential element of ETL since it’s usually carried out on mission-critical data.

There are several types of ETL testing:

  • Completeness checks to see whether any data parts are missing,
  • Validation that the data hasn’t changed in transit and complies with business rules,
  • Metadata testing to ensure it hasn’t changed in transit, 
  • Syntax testing of formally defined data types, 
  • Reference testing against business dictionaries and master data, 
  • Interface and performance testing for the ETL system.

Challenges of ETL Testing 

The fundamental difficulty of ETL testing is its complexity. Large organizations may have ETL procedures comprising hundreds or even thousands of stages and a substantial codebase of custom transformations. It’s hard to comprehend and thoroughly test this complicated environment.

ETL testers face additional challenges, such as data loss or corruption, duplicate data, erroneous or incomplete source data, unstable testing settings, issues with the data model, and vast amounts of historical data, making it difficult to forecast ETL results in the target data warehouse.

ETL Testing vs. Database Testing vs. Manual Testing

ETL testing and database testing require data validation; however, they aren’t the same. ETL testing is often conducted on data in a data warehouse system. In contrast, database testing is frequently performed on transactional systems, in which data is fed from various applications into the transactional database.

ETL testing includes the following operations:

  • Validation of data transfer from source to target system.
  • Data counts are verified in both the source and target systems.
  • Verifying data extraction and data transformation according to requirements and expectations.
  • Checking that table relations (joins and keys) are retained during the transition.

Database testing concentrates on data accuracy, data correctness, and valid values. It involves the following operations:

  • Ensure that primary and foreign keys are preserved.
  • Checking whether the columns in a table contain the correct data values.
  • Validating data correctness in columns. 
  • Verifying missing data in columns. 

The table below compares the essential elements of database and ETL testing.

 

ETL Testing 

Database Testing

Goal

Data ETL for BI reporting

Data validation and integration

Business use case

Used for analytics reporting and forecasting

Used to integrate data from multiple applications

Data type

De-normalized data with fewer joins, more indexes, and aggregations

Normalized data with more joins

ETL Testing vs. Manual Testing

In the manual method, a tester visually inspects the data generated by the ETL process. The ETL procedure is verified if the data output matches what was intended. However, this manual testing technique is not scalable since humans cannot handle more than a few hundred records.

When Should You Use ETL Testing?

ETL testing can be very useful in the following scenarios:

  • When creating a data warehouse for the first time, once the data is loaded.
  • After integrating a new data source into your current data warehouse.
  • Following a data integration project.
  • Following a data migration process.
  • When transferring data for whatever reason.
  • If there are any concerns about the data quality testing in either the source or target systems.
  • If there are any concerns about the performance of ETL procedures.

8 Stages of the ETL Testing Process

1. Understanding Business Requirements

Understanding your organization’s business needs is essential for designing a successful ETL testing procedure. This entails reviewing its data models, business operations, reports, sources and destinations, and data pipelines. This insight allows ETL testers to understand what they are testing and why.

2. Data Source Identification and Validation

In the following step of the ETL testing process, identify your source data and run schema checks, table validation, and other preliminary tests. This ensures that the ETL testing procedure is consistent with the requirements defined by analyzing your business model and processes.

3. Developing and Executing Test Cases

The next phases are source-to-target mapping and test case design, which typically involve the following:

  • Data transformation based on business test scenarios
  • SQL scripts for source-to-target comparisons and execution flows.

Following the preparation and validation of these pieces, test cases are run in a staging environment. Typically, testers can’t use sensitive production data, necessitating real-time fake data. You can generate this data yourself or use test data-generating tools.

4. Data Extraction and Reporting

Next, the team runs ETL tests based on the business requirements and use cases. During test case execution, team members can identify the various types of failures or faults, attempt to duplicate them, and document them with sufficient data and pictures.

During the reporting step, you can enter faults into specific Defect Management Systems and assign them to specified persons for resolution.

To successfully resolve errors, analysts must provide enough documentation to reproduce the test scenario, relevant screenshots, and a comparison of actual and predicted results for each test scenario.

5. Applying Transformations

Next, confirm that the data has been sufficiently changed to meet the schema of the destination data warehouse. In addition to checking the data flow, you should evaluate the data threshold and alignment. The purpose of this is to ensure that the data type for each table and column corresponds to the mapping document.

6. Loading Data into the Data Warehouse

You will perform a record count check before and after migrating data from the staging environment to the data warehouse. Verify that incorrect data is rejected and default values are accepted.

7. Regression Testing (Retesting the Bug)

After correcting the problem, retest it in the same staging environment to ensure no traces remain. Regression testing also ensures no new problems are introduced while repairing the prior one.

8. Summary Report and Test Closure

Finally, close reports documenting bugs and test cases with comments and any relevant documentation. Before closing the summary report, check the choices, filters, formatting, and export functionality.

The summary report describes the testing procedure and outcomes and the reasons why a stage hasn’t been completed.

Download our PDF on how to solve the most common ETL challenges with lakeFS.

Types of ETL Tests

  • Unit testing – evaluates individual ETL process components such as source extraction, data transformation, and target loading. It is crucial to detect flaws in individual components and ensure they perform as planned.
  • Integration testing – it examines how multiple ETL components interact with one another. Integration testing is essential for detecting flaws in component interactions and ensuring that the ETL process runs smoothly.
  • Performance testing – evaluates the ETL process under various load conditions. Performance testing is important for detecting performance bottlenecks and ensuring the ETL process can handle large amounts of data.
  • Regression testing – evaluates the ETL process following any modifications or upgrades. Regression testing is necessary to ensure that modifications or upgrades don’t disrupt current functionality.

How to Create An ETL Test Case

ETL testing is a practice that can be used with many technologies and databases in the information management sector. ETL testing aims to ensure that the data transferred from a source to a destination following business transformation is of high quality. It also includes data verification at several intermediate stages between the source and destination.

While performing ETL testing, an ETL tester will always need two documents:

  • ETL Mapping Sheets – An ETL mapping sheet contains all the information from the source and destination tables, including each column and its lookup in reference tables. ETL testers must know SQL queries since ETL testing may require creating large queries with several joins to check data at any process level. ETL mapping sheets are useful when developing queries for data verification.
  • Database Schema of Source and Target – It should be kept ready to double-check any detail on mapping sheets.

ETL Testing Scenarios

Record Level ETL Tests

  • Record Count Testing – This is the main test to ensure that all accessible records are populated. This test confirms that the ETL procedure has loaded all of the records. However, it doesn’t know if the information in the records is correct.
  • Duplicate Record Testing – Duplicate records occur when primary or unique vital restrictions aren’t implemented in the database. Special ETL tests are required to guarantee that duplicate entries are not created throughout the ETL process.
  • Record Aggregation Test – In many cases, transaction-level records are grouped by time or another dimension. Tests are required to confirm the accuracy of the dimensions used for record aggregation.
  • Row Filter Testing – ETL developers frequently overlook or add filters and occasionally forget to remove filters introduced during testing. Create ETL tests to ensure suitable data filters are applied according to requirements.

Attribute Data Level ETL Tests

  • Data Mapping Testing – During the development of the ETL process, developers may make mistakes in mapping the source and destination properties. This ETL test ensures that the data is supplied with the appropriate target attributes.
  • Numeric and Date Calculations – Many mathematical computations are performed to populate computed fields. This ETL test validates that the computations are accurately performed using the ETL procedure.
  • Expressions – StringStrings are subject to various manipulations and operations, including CONACT, SUBSTRING, and TRIM. This test confirms that the ETL process appropriately handles string transformations.
  • Data Truncation -The ETL process frequently truncates the data, and/or if the target column is smaller, the data may be truncated. This ETL test verifies that string data is not truncated during the ETL process or load time.
  • Data Rounding – This can occur if the datatype is incorrectly specified in the ETL process variables or the destination table datatypes.
  • Formatting Issues – This most commonly occurs with string datatypes, which take data in nearly any format. 

Aggregate Data Level ETL Tests

  • Aggregate Computation – This test ensures that data aggregation is done appropriately.
  • Simple Row Counts – The test checks if the number of records filled does not exceed or exceed the intended number. The destination system’s row count matches that of the source system.
  • Simple Sums – This is done to check that the numbers are right and compare the sums of the numeric values from the source and target.
  • Grouped Row Count – Reconcile counts for distinct groups in the source and destination.
  • Group Sums – Reconcile aggregate amounts for distinct groups from source to goal.

Execution Level ETL Tests

  • Incremental Load – Data is often loaded in increments using delta logic. This ETL test validates that the incremental loads reconcile successfully with the source and that no gaps or overlaps occur.
  • Multiple Execution Tests – You wouldn’t expect the same data to be processed twice. However, in many cases, the data gets reprocessed or accidentally executed. This test validates that successive repetitions of the ETL test using the same data do not result in excess records.
  • ETL Performance Test – The data processing must be completed within the necessary timeframe. The ETL performance test verifies that the ETL processing time is adequate by reviewing the run logs.

ETL Testing Challenges

1. Complex Data Transformations

Transforming data from one format to another may be time-consuming and complicated, especially when working with huge and complex information in multiple forms, which can sometimes feel like comparing apples and oranges.

2. Data Quality Issues

Data quality issues are another problem testers inevitably run into. ETL testing calls for clean and correct data, yet real-world data is frequently chaotic and inconsistent. Poor data quality with issues like duplicate data might provide incorrect findings and jeopardize the integrity of your data.

3. Limited Testing Resources

ETL testing may be resource-intensive, mainly when working with massive datasets or complicated systems.

4. Changing Data Sources

Changing data sources can impact the accuracy and completeness of your data, making long-term data quality maintenance challenging.

5. Integration Challenges

Integrating many systems and data sources can be difficult, especially when working with complicated business processes.

6. Performance Issues

Large amounts of data might slow down processing times or create system crashes, compromising the quality and completeness of your data.

7. Lack of Expertise

ETL testing necessitates a specialized skill set and expertise, which might be difficult for firms with less experience in this field. 

ETL Testing Tools

Several ETL testing tools, both open source and commercial, are available that may help make testing easier and more effective. The tools provide processes and rules for retrieving and processing data, eliminating the need for time-consuming and costly traditional programming approaches.

Another advantage is that ETL testing solutions are natively compatible with cloud data warehousing, ERP, and CRM systems such as Amazon Web Services, Salesforce, Oracle, Informatica, Kinesis, Google Cloud Platform, and NetSuite.

Whether you choose open-source or commercial solutions, here are some things to look for when evaluating ETL testing tools.

  • Graphical interface to aid in the design and development of ETL operations.
  • Automatic code creation accelerates development and reduces mistakes.
  • Data connectors are built-in and may access data stored in popular file formats, databases, bundled applications, or older systems.
  • Content management tools that enable context switching in ETL development, testing, and production settings.
  • Sophisticated debugging tools allow you to follow data flows in real time and provide row-by-row reports.

Best Practices for ETL Testing

1. Test Early and Often

Detecting flaws early in the testing process can save you time and resources in the long run.

2. Use Realistic Test Data

Use realistic test data to guarantee that your testing appropriately represents real-world events.

3. Automate Your Testing

Automating your testing saves time and work while ensuring consistent and thorough testing.

4. Collaborate with Stakeholders

Working with stakeholders ensures that your testing meets company objectives and priorities.

5. Follow a Structured Approach

Using a structured method guarantees that your testing is consistent and comprehensive.

6. Test for Performance

Performance testing guarantees that your ETL procedures can handle enormous amounts of data while remaining fast.

7. Test After Any Changes or Upgrades

Testing after any modifications or updates verifies that everything is still operating correctly and that new changes have not caused any problems.

Roles and Responsibilities of an ETL Tester

As an ETL tester, you play an essential role in verifying the correctness and completeness of data during the ETL process, allowing the company to make educated business choices based on accurate data. 

The following are some of the most significant tasks of an ETL tester:

  • Test Planning and Preparation – Create an overarching testing strategy, plan, and test cases for the ETL process.
  • Data Analysis – Examine source data to verify it is comprehensive, accurate, consistent, and appropriately converted during the ETL process.
  • Test Execution – Run test cases to check the ETL process, ensuring that data is loaded successfully and that all business rules and requirements are followed.
  • Defect Management – This includes identifying and reporting faults or difficulties in the ETL process, as well as collaborating with the development team to address them.
  • Communication – Share testing findings, problems, and other important information with the development team and other stakeholders.
  • Continuous Improvement – Enhance the testing process by adding best practices and new technology to guarantee the ETL process is accurate and efficient.

ETL Testing with lakeFS

The first problem you will face while testing ETLs is the nature of the data lake, as opposed to a data warehouse. We’re writing or streaming large volumes of data to a central place, so the process we evaluate should be based on large amounts of data. This generates the following issues:

Cost

Replicating production data is a time-consuming and costly procedure. Although object storage is inexpensive, it’s not free. Data lakes are often petabytes in size and quickly expand. Copying files into various buckets for a “production-like” test might take hours. Furthermore, if a data lake consumes 100 TB of data on S3, generating a single copy for a continuous testing environment will cost around $25,000 per year. 

Do you want to run many test environments in parallel? Keep multiplying that number.

Reproducibility

To ensure reproducibility, you need to test ETLs on the same data set many times. However, the output of an ETL pipeline shouldn’t vary when executed on the same data collection. When testing an ETL, you want to compare the result set to an expected result set from a prior run. However, because your production data has changed, accessing the new data makes it difficult to ensure the new code functions properly.

Accuracy

The diversity of data can significantly influence the performance of an ETL. For example, if part of the ETL re-partitions data by a given column, the operation may be extremely costly or not, depending on the column values.

Isolation

The “E” component of your ETL is potentially easier to solve. It is possible (although not encouraged) to test your ETL by reading straight from your production object store, where the files are extracted. However, what happens if your transformation requires data deletion? For example, I’d like to test a new retention method that meets GDPR criteria. Are you prepared to take the risk of mistakenly destroying production data?

Automation

ETLs are time-consuming, multi-step, and costly operations. You want to discover flaws (which is much simpler when performing tests on ETLs against the same data source) and detect them early. There should be a mechanism to automatically compare outcomes midway through the process many times rather than waiting for the procedure to finish and then comparing the results.

Solution? Data Version Control with lakeFS

lakeFS is an open-source project that uses software engineering best practices for data engineering. Dev/Test environments and CI/CD are more difficult to adopt in data engineering since data must be maintained in addition to code. lakeFS offers version control across the data lake and employs Git-like semantics to build and retrieve those versions, so any engineer can get to grips with it in minutes.

With lakeFS, you can address the challenges mentioned above:

Cost

Creating a copy of an environment takes milliseconds for data lakes of any size and doesn’t require extra storage. lakeFS allows any developer to test ETLs by branching out of production as many times as they wish, instantaneously and at no additional expense.

Reproducibility

Once you’ve created new versions of your ETL, you can quickly test them against previous changes. This allows you to compare the output of multiple versions of your ETL using the same input.

Accuracy

Because branches are performed directly on production data utilizing lakeFS, ETL testing is performed on identical production data, including all of the intricacies that accompany it. Rest assured, this is exactly what the production environment looks like.

Isolation

Have you ever accidentally erased all of your data? This is no longer a risk with lakeFS. You can delete the branch and begin testing your new code against a fresh branch afresh.

Automation

Run post-commit hooks to test ETLs at each stage of the process. This method not only aids in rapidly identifying difficulties in a given phase within a multi-step ETL. This comes in handy for root cause analysis. 

Check out this practical ETL testing tutorial for more insights!

Conclusion

ETL testing is an important phase in data integration, with several stages and types of testing. By adhering to best practices and employing the appropriate tools and methodologies, you can ensure effective ETL testing and make informed decisions based on reliable data.

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