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ETL in Python

Leverage your Python and SQL knowledge to create a pipeline ingesting, transforming and loading data into a database.

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4 Hours16 Videos48 Exercises
3850 XP

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Course Description

Want to grow your data engineering skills and more efficiently process big data? Well, it’s time to develop your ETL skills. In this course, you’ll learn the foundations of creating pipelines to efficiently extract, transform, and load data into the systems your company commonly uses. You’ll get hands-on experience by helping a fictional private equity firm process the sales data they need to make informed business decisions when buying real estate. Jump in, learn how to create ETL pipelines, and develop one of the most in-demand engineering skills needed in the market.

  1. 1

    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.

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    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
    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.

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  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.

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  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.

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Property price register 2021


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.
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Harvard Business School

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