How to become a Data Engineer: With increasing reliance on data by industries such as IT, Insurance, Banking, Financial Services, Hospital, Healthcare, Internet, and many others, the demand for Data engineers has increased. A new category of software engineers, Data Engineer have thus gained importance due to the rise of Big Data and its key role in prime concerns of businesses. Data engineering is a relatively new profession which is basically a convergence of software engineering and data science.
This article will act as a step-by-step guide to help you know ‘How to become a Data Engineer?’
Data Engineer is a kind of Software engineer who is in charge of data delivery, data storage and data processing. Data Engineer develops, tests, and maintains data storing infrastructure including databases, data processing systems, and data warehouse.
In other words, he is in charge of the creation and maintenance of data workflow as well as data infrastructure underlying thereby.
Data Engineers convert raw data into information required by the data scientist for their analysis. There is a pool of data available which needs to be filtered as per the requirement of the company for analysis. This function of creating data pipelines for transforming and transporting data to relevant information. Data engineer thus need to use advanced programming skills such as data mining, data query, SQL, etc. using programming languages like Python, Scala, and such other languages.
A Data Engineer is a Software engineer with skillsets for data analysis and statistics.
Step-by-step process on how to become a data engineer is as follow:
Data Engineer is a vital job with the responsibility to expand and optimize data and data pipeline architecture and optimize the data flow and collect data for cross-functional teams.
The overview of the responsibilities of a Data Engineer are as follows:
Data Engineering is one of the constantly evolving field presenting various job opportunities. It is a profession that involves the use of new tools, lots of data, analyses and out of the box thinking to help meet the data needs of various data-driven industries.
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