ETL in Python

Course Description

Build Your ETL Skills

Developing your ETL skills will improve your data engineering processes and means that you can work with data more efficiently. This course covers the foundations of creating pipelines to efficiently extract, transform, and load data into your company’s systems. You’ll get hands-on experience by helping a fictional private equity firm process sales data to make data-driven decisions when buying real estate.

Learn to Set up ETL Pipelines

The course opens with an explanation of the ETL process and a deep-dive into data extraction. You’ll then move on to reviewing the ETL pipeline and the tools and techniques you need to transform data. Once the data is in your desired format, you can move it to a clean table and eventually move on to the last stage of the pipeline

loading your data ready to be used.

You’ll finish the course by looking at how the ETL pipeline is used to build useful insight for the fictional company’s shareholders. You’ll look at more complex queries such as aggregation, averages, and max/min functions, before moving on to ways that you can translate raw SQL queries into readable Excel files.

Practice with Popular ETL Tools and Techniques

Throughout this course, you’ll be introduced to ETL tools and techniques that will simplify your workflow and create better structures for your data. These tools include SQLAlchemy, which can help you to perform insert and delete statements on your data, as well as offering aggregation functionality.

What You’ll Learn

Explore the data and requirements

In this first chapter, you’ll be introduced to your role as a data engineer in a private equity fund. You’ll be exposed to the whole ETL pipeline before deep-diving into its first phase: the extraction process.

From raw to clean data

This chapter is all about moving transformed data to a clean table, from which insights can be built. You’ll explore how to create a unique key to perform insert and delete statements on SQLAlchemy. At the end of this chapter you’ll complete the load process, the last step of the ETL pipeline.

Create the ETL foundations

In this chapter you’re going to create some important foundations for our ETL pipeline. In particular, along with data transformation, you’ll start setting up the components needed to communicate with the database.

From clean data to meaningful insights

This chapter will show you how the data the ETL pipeline processes every month is used to build insights, readable by the fund’s shareholders. You’ll explore key SQL components to build more complex queries and create these insights. You’ll also explore libraries that will translate raw SQL queries into more readable Excel files.