Free Certification
Learn to Test Data Before It Breaks Something
The Data Observability and Data Quality Testing Certification is a free, self-paced course in four sessions, about seven hours in total, covering data testing and data observability. The exercises are hands-on and use open-source software, and completing them earns the certificate.
- Length
- About seven hours
- Cost
- Free
- Format
- Online, self-paced
Why this course exists
Most data quality advice tells you what to measure. This course covers what to test.
Testing the data at every point where it changes hands, and watching it once it is in production, is the part that stops an error reaching a customer. That is the subject of all four sessions, and each one builds on the last.
The exercises use open-source software, so nothing here depends on a budget approval.
What's in it
- Four sessions, about seven hours, self-paced
- 28 data profiling quality checks
- 11 best practices for custom data validation tests
- The Data Testing Maturity Model
- Hands-on exercises on open-source TestGen and Observability
What the course covers
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Session 1
Setting the Stage for Data Excellence
The foundational distinction between data testing and data quality, and where testing belongs in the data journey.
- Data testing versus data quality: what each one is for
- Live demonstration of pre-production data testing
- Data profiling and initial data hygiene scans
- The five data observability use cases, and when each applies
- Data checks versus tool monitoring, and why most cases need both
- Finding the dozens of anomalies that affect data quality
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Session 2
Enhancing Data Reliability in Production
Testing data once it is live, covering both data at rest and data in use.
- Data at rest and data in use, and how testing differs
- 28 data profiling quality checks
- 11 best practices for custom data validation tests
- Keeping data integrity in a pipeline that changes
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Session 3
Mastering Data Testing in Development and Migration
Regression and impact assessment during development, and proving a migration did not change the numbers.
- Regression testing and impact assessment
- Functional and performance testing
- Testing as part of a data migration
- Keeping data consistent across development stages
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Session 4
Towards Data Testing and Observability Maturity
Who owns testing, how to manage it, and how to tell whether it is working.
- Roles, management, and metrics for data testing
- The Data Testing Maturity Model
- Measuring testing effort meaningfully
- Connecting testing practice to fewer errors and more trust
Who it's for
Data engineers, data quality managers, and anyone responsible for data that other people depend on.
What you get
Data Observability and Data Quality Testing Certification, once you have completed every session and the exercises that go with them.
Your instructors
Frequently Asked Questions
Common questions about the Data Observability & Data Quality Testing certification
What is the Data Observability and Data Quality Testing certification?
The Data Observability and Data Quality Testing Certification is a free, self-paced course in four sessions, about seven hours in total, covering data testing and data observability. The exercises are hands-on and use open-source software, and completing them earns the certificate. DataKitchen runs it, and there is no cost and no trial that expires into a paid plan.
What does the course cover?
Four sessions. Session one separates data testing from data quality and shows what profiling a new data set tells you. Session two covers production data: data at rest and data in use, 28 data profiling quality checks, and 11 best practices for custom data validation tests. Session three covers regression testing, impact assessment, and data migration. Session four covers roles, metrics, and the Data Testing Maturity Model.
Who should take this course?
Data engineers, data quality managers, and anyone responsible for data that other people depend on. If DataOps as a practice is new to you, DataOps Fundamentals is the better place to start, and this course goes deeper afterwards.
What does the course cost?
Nothing. All four sessions, the exercises, and the certificate are free, and the software the exercises use is open source, so there is no license to buy either.
How long does it take?
About seven hours across four sessions. It is self-paced, so most people work through it over several sittings rather than in one go.
What do I have to do to get certified?
Complete all four sessions and the homework exercises, which use DataKitchen's open-source data observability software. The certification comes from doing the exercises, not from watching the sessions.
Do I need to install anything?
Yes, for the exercises. They run against DataOps TestGen and DataOps Observability, both open source and free to install. There is a quickstart if you want to see them first.
Is this vendor training for DataKitchen products?
The exercises use our open-source tools because you need something concrete to test against. The material itself is about testing practice: what to test, when, and how to tell whether your coverage is any good. That transfers to whatever you run.
How does this differ from the DataOps Fundamentals certification?
This one goes deep on testing and observability, with hands-on exercises. DataOps Fundamentals is the broader introduction: the three pipelines, Data Journeys, and how to implement DataOps across a team. If DataOps is new to you, start there.
It's free, and it starts whenever you do.
About seven hours, self-paced, and you keep the certificate.