Let’s start with the definition of Data Engineering. Simply put, data engineering is the process of designing, developing, maintaing and enhancing the way data is acquired, processed, stored and consumed within an organization.
Data Engineering is an important skill which is quickly becoming a “must have” from a “good to have”. A data engineer is responsible for enabling the optimum usage and consumption of data and analytics.
Main responsibilities of the data engineer may include the following:
- To build and maintain the data flow pipelines
- To monitor and administer the existing data storage solutions
- Maintain consistent data quality by using cleansing and normalization techniques
- To identify and remove redundancies in the existing data platforms
- To optimize the usage of both hardware and software resources
By any means, the above is not an exhaustive list of responsibilities of a Data Engineer. The Data Engineer role is specially important in the current market scenario, since, a lot of organizations are in the process of moving their data systems from “on premises” to the cloud. Many organizations have moved to a hybrid model where they have some of their IT systems on premises and some on the cloud. Therefore, optimization of resources, in other words, “right sizing” is an important part of the Data Engineer role.
Azure Data Engineering 
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