Data engineering is a hot topic in recent years, mainly due to the rise of artificial intelligence, big data, and data science. Every enterprise is transforming in the direction of digitalization. For enterprises, data is full of infinite value. For all the data requirements of organizations, the first thing they need to do is to establish a data architecture/platform and establish a pipeline to collect, transmit and transform data, which is what data engineering does.
In a Data Engineering project , data is collected, organized, processed, distributed and stored. For example, before starting an AI and Data Analysis project, Data Engineers first need to connect to various data sources to collect data, then work on data transmission and transformation, and finally store the data in a designated place(e.g. a data warehouse) , in a designated format(e.g. a database). We call the full process “a data pipeline”. From the endpoint where the final data is stored, the AI or Data Analysis team will build their connection to the data and start their data activities. In many cases data engineering also automates this data pipeline.
