Data engineering
===================

.. _data:

.. figure::  /images/Domains/data.jpg
   :align:   center

**Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information**

In order for that work to ultimately have any value, there also have to be mechanisms for applying it to real-world operations in some way. Those are both engineering tasks: the application of science to practical, functioning systems.

Data engineers focus on the applications and harvesting of big data. Their role doesn’t include a great deal of analysis or experimental design. Instead, they are out where the rubber meets the road (literally, in the case of self-driving vehicles), creating interfaces and mechanisms for the flow and access of information. They may be experts in:

* System architecture
* Programming
* Database design and configuration
* Interface and sensor configuration

Although data engineers don’t always get the glory of coming up with crazy insights by querying and combining big data sources, their work is important in building the data stores that are used in that work, and in taking those insights and putting them to practical use.