Join 📚 Kevin's Highlights
A batch of the best highlights from what Kevin's read, .
**The Perils of Comparison:**
Excessive social comparison leads to
decreased self-esteem,
anxiety, and
depression.
Measuring our worth solely by
the amount of work completed
damages our mental health and satisfaction.
Are You Really Worthy?
Swirling Visions
In [theoretical computer science](https://en.wikipedia.org/wiki/Theoretical_computer_science),
the **CAP theorem**, also named **Brewer's theorem** after computer scientist [Eric Brewer](https://en.wikipedia.org/wiki/Eric_Brewer_(scientist)),
states that any [distributed data store](https://en.wikipedia.org/wiki/Distributed_data_store)
can provide only [two of the following three](https://en.wikipedia.org/wiki/Trilemma) guarantees:
[Consistency](https://en.wikipedia.org/wiki/Consistency_model)
Every read receives the most recent write or an error.
[Availability](https://en.wikipedia.org/wiki/Availability)
Every request receives a (non-error) response, without the guarantee that it contains the most recent write.
[Partition tolerance](https://en.wikipedia.org/wiki/Network_partitioning)
The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes.
CAP theorem
wikipedia.org
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;
...catch up on these, and many more highlights
Readwise