My name is Don and I am something between a data engineer and a data scientist. I automate repetitive tasks, generate insights from the data, manage projects and I often help in an advisory role for these processes. I especially enjoy the creative process required to solve problems using data.
Read moreThese instructions build on what has been discussed in ELT, ETL and Data Pipelines. In that guide, we discussed the problems that arise in storing and using data for a company. In response to those problems, we introduced the concept of Data Pipelines, which helps the company become better aware of the data loading steps and incorporating these steps in the most optimal way to create a Data-driven Culture. We also discussed some specific tooling that can be used to properly deploy Data Pipelines.
Now that we understand the concepts behind Data Pipelines, we will now apply them to implement a functioning Data Pipeline. Just like most of our data engineering processes, we follow a step-by-step plan and provide an implementation strategy for each step.
Hopefully a step-by-step plan will give you a solid foundation when you are constructing your own Data Pipeline as well as the implementation methods. You can find the whole code on our Giftlab.
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By Young Mavericks’ Data Engineer Don de Lange
Data Engineers must ensure that technological solutions for companies can actually be implemented. In order to fulfil these technical promises in an increasingly complex data-driven world, like many other Data Engineers, I use various software and data tools. Docker and Kubernetes, two leaders in the field of open source technologies, help to build, manage and scale apps in containers. In this blog I explain what Docker and Kubernetes are, why more and more companies are taking advantage of their expertise and how you yourself can take advantage of these platforms.
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Hey everyone, I’m Kevin Bowey. Thanks to Young Maverick’s October Data Engineering Traineeship, I am currently enjoying working as Junior Data Engineer. You might think to yourself: wow, starting a traineeship at 32? Hell yes! Many of my fellow trainees dove into the world of Big Data during or immediately after their studies. My path to machine learning, however, was a lot less straightforward – a ‘detour’ that gave me unique skills as a Data Engineer.
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Yaleesa’s (28) interest in the wonderful world of machine learning was peaked at the end of her BA in Information Science. Yaleesa: “For my ambitious graduation research, I was looking for an answer to my specific problem and ended up with a supervisor.He convinced me of the unique potential of machine learning. Although I quickly discovered that machine learning is no walk in the park, I feel at home in the IT world – and more specifically with Young Mavericks.”
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