Data engineering is an arm of data science that is concerned with data optimization, data monitoring, data retrieval, and data distribution in an organization. This is to guide an organization through the right decision-making path(s).
A data engineer is someone with credible analytical skills, good technical abilities, sufficient knowledge of structural query language(s), database design, and other programming language skills. A data engineer is required to have good communication skills so that they can explain data trends and datasets to the stakeholders of an organization. A data engineer should also understand the objective of an organization, so as to develop an algorithm that is suitable for accessing organizational datasets.
Big data projects:
Data engineers collect, analyze, and visualize a large number of datasets. They engage with various toolsets, techniques, and cloud-based platforms to turn data into insights.
Building and maintaining ETL pipelines:
ETL pipelines are used for the extraction, transformation, and loading of data, and are designed through algorithms. They are tools that make crucial data accessible for an entire organization.
Design and support BI platforms:
BI platforms are business intelligence platforms that help in gathering, understanding, and visualizing data. These platforms make data available for sets of applications that have a need for them.
Handle large datasets:
A data engineer understands an organization’s objectives and sorts out large datasets to meet those objectives and solve business problems.
Data engineers interpret sorted data through graphs or charts, so as to transfer sorted data as insights and information to stakeholders.
Prepare datasets for modeling:
Data engineers use datasets to predict future events, through predictive modeling and also use datasets to perform prescriptive modeling.
Data engineers collaborate with other data scientists to create valuable data services for an organization. A data engineer is a team player.
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