Shaping future data professionals — Together with Tampere University

Agile Data Engine’s journey with Tampere University’s Information and Knowledge Management students 

What the course was and why it matters now 

The Data Warehousing in Practice course contained a high-level introduction to data warehousing, data modeling, and how to lead data projects.  The main part of the course was the Agile Data Engine for Data Engineers training. It was a perfect mix of high-level theoretical understanding and practical application with ADE. In practice, we walked through a simplified data warehouse project using Agile Data Engine and Snowflake. The idea was simple: ADE handles the heavy lifting — generating complex database queries, managing dependencies, and ensuring consistency across the project. This means less time spent on repetitive tasks and more time for meaningful work. And the best part? Learners can grasp the basics of data warehousing in just a few days!

Why we partnered with Tampere University 

It all started as a bold idea. University studies are often high-level and technology-agnostic, as they should be. Practical examples and exercises are sometimes left aside while the bigger picture takes priority. On the other hand, we at Agile Data Engine wanted to share the value of data warehouse automation, acceleration, and our all-in-one tooling approach, besides acknowledging the more theoretical issues. Then the idea emerged — we have a strong training program, and the university has students eager to learn.

How ADE’s training fits into the curriculum 

In the later stages of their master’s studies, information and knowledge management students already understand knowledge management and business analytics in general but also understand the need for a solid background knowledge in data modeling, data warehousing, leading data projects, and the basics of SQL.  One of the main goals was to highlight the importance of data modeling in a data warehouse and help students understand how automation and standardization work in practice. The ADE training, with concrete examples, nicely complemented the high-level understanding students had gained at university. 

 Learn more about the Agile Data Engine trainings

Iteration, Testing, and Continuous Learning 

The whole process started with a plan. We had several discussions with the university and Agile Data Engine teams to find the right approach — staying at a general level but offering clear examples of data modeling with Data Vault, data work with ADE, and comprehensive understanding of how data warehouse project proceeds. The course progressed from conceptual level to the detailed steps of executing a simplified data warehouse project with ADE. 

We then ran a test course with a small group, iterated along the way, and made improvements based on their feedback. The testing phase proved its value once again, helping us identify pitfalls early. Because there was enough time between the test and the actual course, we were able to refine the materials thoroughly. 

The course lasted one month and included lectures on Data Warehousing, Data Vault Modeling, and Leading Data Projects. The core was ADE exercises completed during class. These sessions combined short explanations of modeling and ADE usage while focusing mainly on practical execution. The idea was to continuously connect theory and project work so students could clearly understand why each step was taken. 

At the end, students faced the “final boss” — the Practice Assignment. Unlike the guided exercises, this assignment provided far fewer direct instructions. Nevertheless, the students successfully completed the project and earned their ADE certificates. 

“ADE is powerful tool; it automatically generates complex database queries through a graphical user interface. The business case as part of the exercises gave a good reason why and what we did in practice.” 

What Students Gained 

Based on the feedback, the students acquired a comprehensive understanding of the critical role that effective data warehousing plays in supporting business operations and strategic decision-making, they learned how to use ADE, understood the practical importance of data modeling, and gained new perspectives on real data work. Since many were close to looking for their first role in data landscape, comments connecting the course to real-life practices were especially insightful. They could see the entire data flow, understand how changes propagate, and experiment without fear of breaking things. The learning curve flattened, and confidence grew. As one student put it:

“After this course, I understood that you don’t need to know how to code to design and build data warehouse solutions.” 

That’s the real power of ADE — it democratizes data work. Whether you’re a seasoned engineer or a business-minded student taking your first steps, the platform meets you where you are and helps you build something real. And who knows — the certificate may help them land their first ADE project role. 😊 

Feedback, outcomes, and what comes next 

One of the recurring themes in student feedback was surprise — surprise at how accessible data warehousing can become with the right tooling. Traditional approaches often require deep technical expertise and significant manual effort. ADE changes that equation. With its easy user interface and automation capabilities, ADE allows users to focus on what truly matters: understanding the data, designing solid models, and making informed decisions. 

Looking back, this was a win-win collaboration. Agile Data Engine received valuable feedback and ideas to improve our training program, while for university students this offered the opportunity to enhance their knowledge of data warehousing by engaging in a hands-on, practice-oriented learning environment and a new connection to working life. From a softer perspective, focusing more on the “why” questions strengthened our own understanding of Data Vault and data modeling. This course also expanded awareness of ADE for the next generation of data professionals and leaders. 

Want to hear more? Please contact us for more information:
Ville Keskinen (Agile Data Engine) & Jussi Myllärniemi (University of Tampere)