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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;
Through decades of corporate greed, production has become almost entirely separated from capital, meaning that executives (and higher-ups) are no longer able to understand the nature of the businesses they are growing.
By not actively participating in the creation of the labor that enriches them, they are unable to truly understand trends within their business, because they’re only aware of how it works on the most distant level.
And because they do not participate, they do not appreciate *profit* — they only appreciate *more profit than they previously had.*
Absentee Capitalism
Ed Zitron
“I’m sick of writing everything in numbered order,” Tom said, listlessly
For Many Years I’ve Been Collecting Tom Swifties and Croakers...
Adam Sharp
...catch up on these, and many more highlights
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