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Python [decorators](https://realpython.com/primer-on-python-decorators/) are another popular and convenient use case for inner functions, especially for closures.
**Decorators** are higher-order functions that take a callable (function, method, class) as an argument and return another callable.
Python Inner Functions: What Are They Good For?
Real Python
9. What distribution is typically used to estimate the mean of a normally distributed population when its standard deviation is unknown?
Fifteen Questions to Test Your Statistics Knowledge
Keith McNulty
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;
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