Essential Skills for Data Engineers: Tools and Libraries Insight
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Chapter 1: The Role of Data Engineers
To thrive as a Data Engineer and boost your market value as well as your salary, acquiring specific skills is essential. In previous articles, I discussed the importance of database knowledge and proficiency in programming languages. This article will delve into the tools and libraries crucial for Data Engineering.
Section 1.1: Tools in the World of Data Engineering
When exploring Data Engineering, especially in the realm of Big Data, we find ourselves dealing with Distributed Computing. Established tools like Apache Hadoop, Apache Spark, and Apache Flink enable simultaneous data processing and analysis across multiple servers. Although Hadoop remains a reliable option, its age and the need for extensive custom administration have led to increased competition from cloud-based SaaS solutions. For further reading, check out these discussions:
Is Hadoop Still Relevant?
What Lies Ahead for the Big Data Ecosystem?
The first video titled "What Skills Do Data Engineers Need To Know" offers insights into essential competencies for aspiring Data Engineers.
In this environment, cloud service providers present a variety of user-friendly services that benefit Data Engineers and related roles like Data Scientists. These services facilitate the creation of data integration and platforms such as Data Lakes, Data Warehouses, or Data Lakehouses.
Section 1.2: Building Data Platforms in the Cloud
Typically, organizations focus on a specific cloud environment to construct their data platforms, employing Data Engineers or Data Architects for this purpose. For more guidance, see this article on establishing a data mesh within the Azure Cloud:
Building a Data Mesh on Microsoft Azure
How to Establish a Robust Data Platform on Azure
However, data can originate from diverse sources, including other cloud environments or on-premise systems. For Data Engineers, utilizing a data integration tool that connects to as many systems as possible is advisable. Major cloud providers offer various solutions, but independent vendors such as Alteryx, KNIME, and Talend are also popular choices, offering intuitive drag-and-drop Data Engineering alongside coding capabilities, particularly in Python.
Subsection 1.2.1: Importance of Programming in Data Engineering
This example illustrates that while drag-and-drop tools can simplify certain tasks, knowledge of programming languages remains crucial. Some tasks, such as statistical analysis or machine learning, may require integration with IT systems using languages like R or Python. For a deeper dive, you might find this article about useful Python libraries in Data Engineering intriguing:
My Top Big Data Python Libraries
Which Libraries Can Assist in Big Data Processing?
Chapter 2: Summary of Key Insights
As a whole, Data Engineers are increasingly aided by various tools and services. The trend shows that organizations are leaning towards solutions from prominent cloud providers, offering comprehensive data integration and modern big data platforms from a singular source. Nevertheless, a solid foundation in programming languages and libraries is still necessary for Data Engineers to tackle specialized use cases.
The second video, "Beginner Level Skills for Data Engineering (2024)," explores foundational skills that budding Data Engineers should acquire.