Python Data Engineering – Learn Data Engineering with Python: visualize, manage and manipulate data at scale

In this chapter we are going to learn more about the definition of data engineering and what that really takes into account. You will learn the tasks and core competencies of data engineers and we will introduce you to a few key concepts like ETL

This chapter is fully focused on the tools you can use with Python to manage large datasets of data. You will learn how to use Jupyter notebooks, Numpy and Pandas.

In this chapter, we dive deep into data extraction using various techniques. We will use Python and SQL in a variety of environments and use cases.

Exploratory Data Analysis is an approach to analyzing datasets that involves summarizing their main characteristics, often using visual methods. It’s a critical first step in data analysis, allowing analysts to discover patterns and more

In this chapter, you will learn how to clean up data before working on it. This is usually related to the removal of columns, information, and other data that is not needed.

Now that you have cleaned your data it’s time to visualize them and it’s chapter is all about learning how to use a few Python libraries to easily visualize data and build any type of chart.

A data pipeline automates the flow of data from sources to storage and analysis. It is the final result of everything we have seen in this path: It involves extraction, transformation, and loading (ETL) processes to prepare and move data efficiently.