Welcome to Tech Station, SDG Group’s hub for uncovering the latest innovations in data and analytics! In this article, we explore Robot Framework, a powerful open-source tool for test automation. We’ll discover how its intuitive approach and versatility make it an ideal tool not just for traditional software testing, but also for ensuring data quality and integrity in complex ecosystems like Snowflake and dbt Cloud.
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Robot Framework is an open-source, test automation framework designed mainly for acceptance testing and acceptance test-driven development (ATDD), supported by the Robot Framework Foundation.
It uses a human-friendly keyword-driven approach to create test cases, making it easy for non-developers and developers alike to write and understand suits of tests.
It was initially developed for testing software, but its versatility allows it to automate various processes, including web applications, APIs, databases, and other interfaces.
Robot Framework integrates with different tools for comprehensive automation without licensing fees and supports extending through libraries in Python, Java, and other languages.
As already mentioned, Robot Framework has an extremely versatile and intuitive syntax that simplifies the test creation and maintenance.
Moreover, each keyword component is reusable in different test scripts, which makes Robot Framework a powerful, modular tool.
However, one of the major benefits is cross-technology testing, given that it seamlessly integrates with multiple technologies and systems.
Users can test web applications through Selenium, APIs with Requests library, mobile apps thanks to Appium library, CI/CD Integrations, since it works well with Jenkins, Argo WF, and other CI/CD tools to include tests in pipelines, and even desktop applications with White Library.
It might be spontaneous now wondering when exactly to use Robot Framework to obtain the most of it. For instance, the best fits would be:
Robot Framework is implemented with Python, hence the latter must be installed on the machine.
Once that is verified, Robot Framework can be installed as follows.
Additional information about the installation can be found here.
Other requirements depend on the packages and tools the user wants to integrate it with. Let’s have a look at some specific use cases in the next section, though.
This use case example aims at describing how efficient the robot test could be if included in a dbt Cloud job.
Consider a job defined in dbt Cloud to transform and load data into a data warehouse in Snowflake.
Let’s develop a test that must trigger the dbt cloud job, using a POST request, and extract the run_id of the job of interest, to verify its status before proceeding.
The last goal of the test is to validate the transformed data in Snowflake, by checking a) row count, b) null values, and c) data consistency for a given condition (e.g. no negative values).
Here is an example of how to define a robot test. First, a script with the extension .robot must be created.
A snowflake_connector.py script must be defined as well, to connect to the DWH on Snowflake.
To run the test, use the robot command:
The results of the test in case of a successful output will be:
In case null values are found, the output will be:
Another remarkable application of robot tests might be on quality checks that must ensure all foreign key references in a table match the valid primary keys in the related table.
That is crucial for maintaining the integrity of relationships in relational databases.
Let’s take into account table CUSTOMERS in a Snowflake database.
The primary key is customer_id. The dependent table is ORDERS, with customer_id as a foreign key.
The validation query to assess as expected output 0 — indicating that every customer_id in ORDERS matches a valid customer_id in CUSTOMERS — is the following:
To be able to debug failures encountered the Log To Console or Log keywords can be used when defining the test. That will allow the tester to print intermediate results and see what’s going on during test execution.
Alternatively, to enable debugging, run the test with--loglevel flag set to DEBUG to capture detailed logs:
robot --loglevel DEBUG <name_of_the_test>.robot that will provide detailed information about each step, including input/output of queries, variable assignments and HTTP requests if APIs are used.
Another possible option would be the Test Artifact available online.
robot --outputdir ./logs data_quality.robot
Outputdir will allow the tester to see a breakdown of each step in log.html after the test run. If that is now enough, in report.html all results are summarized.
In today’s world, where automation plays a key role in boosting productivity, Robot Framework stands out as a valuable partner for companies looking to improve their operations and keep quality high.
Its ability to expand, user-friendly nature, and free access make it a must-have for test automation across many fields, from software creation to data quality checks.
Major perks like working on any platform, using keyword-based commands, and playing well with other tools let teams tackle tough problems instead of getting stuck on technical details.
Whether one is running tests for a Snowflake data warehouse keeping an eye on dbt jobs, or fixing complex processes, Robot Framework offers a strong solution that grows with the user’s needs and adapts.
Robot Framework does more than just test — it makes debugging easier, creates detailed reports, and helps teams work together with easy-to-read scripts.
This makes it a driving force for new ideas and top-notch results. By tapping into what it can do, companies can boost quality, speed up delivery, and in the end, give more value to those who matter.
As more businesses turn to automation, Robot Framework’s ability to bend and not break puts it at the heart of achieving success in day-to-day operations.
Ready to enhance your data quality and streamline your testing processes with powerful automation? Contact us for a personalized consultation and discover how our expertise in data engineering and test automation can help you implement robust, scalable, and easy-to-maintain testing solutions with Robot Framework.