In 2024, the field of Data Engineering continues to evolve rapidly, with the emergence of new tools and technologies designed to effectively manage ever-growing volumes of data. Here's a look at the main tools that will dominate the data engineering landscape this year.
With the rise of data in all sectors, two key professions have emerged: Data Engineering and Data Science. Although their missions are complementary, their roles and skills are often confused. So what are the real differences between these two professions? In this article, we explore their responsibilities, their tools, and how they work together to transform data into strategic business value.
Today, the role of data engineer is essential for companies. This professional designs and maintains the infrastructure needed to ensure that data is properly collected, secure and easily exploited by other users. Their work goes far beyond simple data collection: they transform raw data into ready-to-use information that can be accessed by other teams. Although often confused with data scientists and analysts, the data engineer has a unique and crucial mission: to ensure the reliability and fluidity of data so that strategic decisions are based on solid and secure foundations.
But what exactly is Data Engineering, and why is it essential? Let's delve into the basics of this discipline, its fundamental principles and its strategic role in digital transformation.
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