PART 3— Data Engineering Tool and Library Knowledge

What Skills do Data Engineers need?

How to increase your Market Value and Salary

In order to be successful as a Data Engineer and thus increase your market value and salary, you need certain skills. I have thought about illuminating these in more detail in various articles. Last time I wrote about how Data Engineers need database knowledge and programming language knowledge. This time, I will focus on Data Engineering tools and library knowledge.

When it comes to (Big) Data Engineering we enter the field of Distributed Computing. Classical Tools such as Apache Hadoop, Apache Spark or Apache Flink make it possible to process and analyze data simultaneously on several servers. Hadoop is still a solid tool, but it is aging and also requires more custom administration. Thus, it is getting competition from cloud-based SaaS solutions. Read here more about it:

Here, cloud providers offer Data Engineers and the subsequent processes and roles such as the one of a Data Scientist many useful and easy-to-use services. For example, you can use a wide range of tools to set up data integration and data platforms such as Data Lake, Data Warehouse or Data Lakehouse.

In most cases, a company specializes in a cloud by building such a data platform with the help of Data Engineers or Data Architects. Here is an article about how you can build a data mesh in the Azure Cloud.

However, the data can of course come from different sources, such as other clouds or even on-premise. As a Data Engineer, it is then advisable to use a data integration tool that can open up as many systems as possible. The large cloud providers also offer solutions here, but independent specialized providers such as Alteryx (Alteryx — a worthy Data Platform?), KNIME and talend also have many customers here and offer drag-and-drop Data Engineering but also the implementation of code, e.g. Python.

This example illustrates quite well that even if drag-and-drop tools are used for special requirements, programming languages knowledge is still necessary. After all, some statistical analysis or machine learning programs could also be integrated into an IT landscape. Here, usually R or Python will be helpful. There are interesting libaries for Python that you can use in the area of Data Engineering and Big Data. In this case, you might find this article interesting:

Summary

So all in all, Data Engineers are increasingly supported in their work by tools and services. The trend is clearly that companies are using services from large cloud providers for this purpose. Here, you often get solutions for data integration and the subsequent modern big data platform from a single source. Nevertheless, Data Engineers still need knowledge of programming languages and libaries to implement special use cases.