What Is Data Engineering?
Big data is changing how we do business and created the need for data engineers who collect and integrate large quantities of data.
Data engineering is the process of designing and building systems for collecting, storing, and analyzing data at scale.
Understanding what data engineering is should have helped you guess some of the responsibilities of data engineer, such as;
Acquiring datasets that align with business needs.
Develop algorithms that transform data into useful information.
Create new data analysis tools and data validation.
Collaborate with management to understand organization objectives.
Tools for Data Engineering
Programming language: Proficiency in a programming language preferably one of Python, Scalar, and Java.
Databases and SQL: Good understanding of database design and ability to work with databases both relational databases such as MySQL and PostgreSQL and non-relational databases such as MongoDB.
Automation and Scripting: Familiarity which Shell scripting and data processing and the command line.
Cloud Computing: Understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services
Knowledge of operating systems and networking.
Data warehousing fundamentals: Modern data stacks like ELT (extract, load, and transform) systems such as dbt.
A portfolio is often a key component in a job search, as it shows recruiters what you can do. You can add data engineering projects you've completed independently or as part of coursework to a portfolio website.
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