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When we unpack the common threads of how various people define data engineering, an obvious pattern emerges:
a **data engineer**
*gets data, stores it, and prepares it for consumption*
by **data scientists**, **analysts**, and others.
We define data engineering and data engineer as follows:
**Data engineering** is
the *development*, *implementation*, and *maintenance*
of **systems** and **processes** that take in raw data
and produce high-quality, consistent information
that supports downstream use cases,
such as analysis and machine learning.
**Data engineering** is
the intersection of
*security*,
*data management*,
*DataOps*,
*data architecture*,
*orchestration*, and
*software engineering*.
A **data engineer**
*manages the data engineering lifecycle*,
beginning with getting data from source systems and
ending with serving data for use cases,
such as analysis or machine learning.
Fundamentals of Data Engineering
Reis, Joe;Housley, Matt;
Symmetric cryptography is fast and efficient
but can be vulnerable if the key is compromised.
Diagram showing how symmetric encryption works
What Is Encryption at Rest? Explained for Security Beginners
David Clinton
we get lost in our thoughts
far more often than
we get lost in the physical world,
and since
we rarely pay attention to the direction of our thoughts,
we can get lost for long periods of time without realizing it.
The Three Rooms
Kevin Murphy
...catch up on these, and many more highlights
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