Analytical workloads can generally be divided into three main areas: data engineering, business intelligence, and advanced analytics.
Data engineers are responsible for creating data pipelines that enable data consumers, such as data analysts, data scientists, and machine learning engineers to deliver insightful reports and machine learning, or artificial intelligence, models. It could easily be argued that, without some form of data engineering, getting significant value from complex or large data can quickly become an inefficient and overwhelming task.
The most ideal scenario for a data engineer when it comes to data acquisition is...
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