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Part 1

Computer science fundamentals

As a data engineer, you will work most of your time with Linux® and its toolbox. As such, you need to master the basics properly before we can continue. Once you get accustomed to this new environment, we’ll continue with a basic programming course in Python®.

Then we dig deeper into how computers work and how to design and develop the software properly.
In this context, you need to understand the purpose of an operating system and the concepts behind it.
Since we have never been so connected before in the history of mankind, computer networks and, more specifically, the main components and protocols that make up a physical network have to be addressed.
Finally, we introduce the concepts of data transport, data modeling, data storage, and data security.

Parallel to these tracks, we will discuss the impact all these technological evolutions have had and still have on our daily life and work.

In part one, we will focus on the fundamentals:

  • Linux and tools
  • Programming in Python
  • Operating system concepts
  • Computer networks: i.e., router, switch, TCP, UDP, IP, Ethernet, ARP, …
  • Relational databases
  • Impact of computing

Part 2

Data engineering

Once you have mastered these fundamental concepts, we will start comparing legacy approaches to cloud-native practices as they apply to data-intensive computing and applications.

Data-intensive computing requires a cluster of distributed virtualized computers. In analogy to an operating system on a single computer, new tools are required to manage and monitor these clusters.

In a cloud-native environment, all these clusters are (inter)connected through a computer network, where topics like scalability, availability, reliability, and security by design are essential to understand.

Dealing with the growing complexity and importance of data requires new strategies, algorithms, design principles, and tools that will allow you to handle data delivery “at least once”, “exactly once”, or “at most once” in an economically justifiable manner.

And finally, as we process huge amounts of potentially privacy and security sensitive data in data engineering, it is essential to address data ethics.

In part two, we will focus on:

  • Containers: Docker
  • Container orchestration: Kubernetes
  • Cloud-native and hybrid offerings: i.e., AWS, Exoscale,…
  • Event frameworks: i.e. MQTT, Apache Kafka,…
  • Programming languages: i.e., Golang, Scala,…
  • NoSQL databases, block storage, object storage,…
  • Automated development: CI/CD
  • Cloud economics
  • Data ethics

Meet our head coach

Bram Stes

Bram has been our core R&D lead at Klarrio since the beginning and became one of the main figures behind tutorrio.

He helped us to design a highly data engineering learning space that goes beyond technology. It builds upon our belief in open-source, while addressing cloud economics and data ethics.

Learn, code,

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