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 Exercises10,359 Learners
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Build Your ETL SkillsDeveloping 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 PipelinesThe 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 TechniquesThroughout 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.
Explore the data and requirementsFree
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.
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.Let's talk with the database50 xpSQLAlchemy core components50 xpEngines and sessions100 xpDatabase tables50 xpTable class definition100 xpColumns definition100 xpData cleaning50 xpLower string and date format100 xpPrice and description100 xpPut transform operations together50 xpSetup base script100 xpCreate tables100 xpTransform 'em all!100 xp
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.
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.Operators50 xpSales for Dublin and Cork100 xpFirst month 2021 sales100 xpSqlalchemy func50 xpAggregate functions50 xpAverage, max and min functions100 xpCreate the insights50 xpCreating the insights view100 xpHow many counties?50 xpWorking with Excel files50 xpCreate a simple Excel file100 xpAdd a table into Excel file100 xpExport monthly insights100 xpWrap-up50 xp
DatasetsProperty price register 2021
Stefano FrancavillaSee More
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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