Skip to main content

ETL in Python

Leverage your Python and SQL knowledge to create an ETL pipeline to ingest, transform, and load data into a database.

Start Course for Free
4 Hours16 Videos48 Exercises8,003 Learners3850 XP

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies


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.

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.
  1. 1

    Explore the data and requirements

    Free

    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.

    Play Chapter Now
    Introduction to ETL in Python
    50 xp
    The ETL process
    100 xp
    Downloading a ZIP file
    100 xp
    Exploring a ZIP file
    100 xp
    Ask the right questions
    50 xp
    Reading from a CSV file
    100 xp
    Writing to CSV
    100 xp
    Extracting
    50 xp
    Downloading the new dataset file from web
    100 xp
    Project folder structure
    50 xp
    Extract 'em all!
    100 xp
  2. 2

    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.

    Play Chapter Now
  3. 3

    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.

    Play Chapter Now
  4. 4

    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.

    Play Chapter Now

Datasets

Property price register 2021

Collaborators

hadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9
Hadrien Lacroix
Stefano Francavilla Headshot

Stefano Francavilla

Stefano is the CEO and co-founder of Geowox.

Stefano is the CEO and co-founder of Geowox, a company using AI and big data to value residential properties. In a previous life, he studied Computer Science at the polytechnic university of Milan while founding a software development company. He then worked as a product engineer at Intercom, advised portfolio startups at Growing Capital, a seed investment firm.
See More

What do other learners have to say?

Join over 10 million learners and start ETL in Python today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.