-
7 & 21 november 2025 (English)
-
6 & 27 March 2026 (English)
-
16 hrs (class) + 4 hrs (preparation)
-
Price 鈧� 950 (VAT exempt)
2-day masterclass Data Engineering
After the masterclass Data Engineering participants will be equipped with the knowledge and practical skills needed to engineer state-of-the-art data platforms that support data-centric AI applications, ensuring their organization is at the forefront of technological innovation.
What to expect
"Power is nothing without control." - Pirelli
Likewise, AI is nothing with good data and a data platform on which we can develop machine learning applications. Since the boom of AI in the 2000s, we see that the requirements for data platforms have evolved faster than our current development and engineering practices. This situation has created a siloed landscape composed of hundreds of products developed and maintained as monoliths, with limited reuse between systems. It has also affected the end users, who are often required to learn the idiosyncrasies of dozens of incompatible SQL and non-SQL API dialects, and settle for systems with incomplete functionality and inconsistent semantics.
New solution patterns and practices are needed to engineer modern platforms such that data professionals and organizations can work effectively with data. The good news is that there are quite a few techniques that can be used for modern data engineering. The challenge, however, is that these techniques come from widely differing fields of expertise.
What you will do:
In this 2 day masterclass, we will cover the breadth and depth of how to engineer modern data platforms. Participants will learn:
- How to use the concept of the 'composable data stack' as a means to decrease development and maintenance cost and pick-up the speed of innovation;
- How to design modern data platforms that are federated and decentralized, to allow for privacy-aware federated learning and secure multi-party computation
- How to use various standards and levels of interoperability (semantic, syntactic) to move towards FAIR data sharing, where data is Findable, Accessible, Interoperable and Reusable
- How software engineering practices such as DevOps, MLOps, observability can be used to operate such data platforms.
You walk away with
Participants in this masterclass will leave with a comprehensive understanding of modern data engineering principles and practices. And a certificate of attendance.
Do you fit the profile?
- Data professionals (data scientist, BI specialist, data engineers, product owners) seeking to deepen their knowledge in this area
- Experience with any analytical programming language is strongly advised (SQL, Python, R). Examples and groupwork will be done using Python and open source libraries
- Basic knowledge of cloud infrastructure is preferred
Requirements
- Experience with Analytical Programming Languages: Familiarity with SQL, Python, or R is strongly recommended, as the masterclass will involve practical examples and group work using Python and open-source libraries.
- Basic Knowledge of Cloud Infrastructure: While not mandatory, a foundational understanding of cloud computing principles will be beneficial for participants, as the masterclass will cover cloud-based data platform designs.
