Note
If you're looking for Airflow videos from the 2022 edition, check the 2022 cohort folder.
If you're looking for Prefect videos from the 2023 edition, check the 2023 cohort folder.
Welcome to Week 2 of the Data Engineering Zoomcamp! 🚀😤 This week, we'll be covering workflow orchestration with Mage.
Mage is an open-source, hybrid framework for transforming and integrating data. ✨
This week, you'll learn how to use the Mage platform to author and share magical data pipelines. This will all be covered in the course, but if you'd like to learn a bit more about Mage, check out our docs here.
- 2.2.1 - 📯 Intro to Orchestration
- 2.2.2 - 🧙♂️ Intro to Mage
- 2.2.3 - 🐘 ETL: API to Postgres
- 2.2.4 - 🤓 ETL: API to GCS
- 2.2.5 - 🔍 ETL: GCS to BigQuery
- 2.2.6 - 👨💻 Parameterized Execution
- 2.2.7 - 🤖 Deployment (Optional)
- 2.2.8 - 🗒️ Homework
- 2.2.9 - 👣 Next Steps
In this section, we'll cover the basics of workflow orchestration. We'll discuss what it is, why it's important, and how it can be used to build data pipelines.
Videos
- 2.2.1a - What is Orchestration?
Resources
In this section, we'll introduce the Mage platform. We'll cover what makes Mage different from other orchestrators, the fundamental concepts behind Mage, and how to get started. To cap it off, we'll spin Mage up via Docker 🐳 and run a simple pipeline.
Videos
- 2.2.2a - What is Mage?
- 2.2.2b - Configuring Mage
- 2.2.2c - A Simple Pipeline
Resources
Hooray! Mage is up and running. Now, let's build a real pipeline. In this section, we'll build a simple ETL pipeline that loads data from an API into a Postgres database. Our database will be built using Docker— it will be running locally, but it's the same as if it were running in the cloud.
Videos
- 2.2.3a - Configuring Postgres
- 2.2.3b - Writing an ETL Pipeline : API to postgres
Ok, so we've written data locally to a database, but what about the cloud? In this tutorial, we'll walk through the process of using Mage to extract, transform, and load data from an API to Google Cloud Storage (GCS).
We'll cover both writing partitioned and unpartitioned data to GCS and discuss why you might want to do one over the other. Many data teams start with extracting data from a source and writing it to a data lake before loading it to a structured data source, like a database.
Videos
- 2.2.4a - Configuring GCP
- 2.2.4b - Writing an ETL Pipeline : API to GCS
Resources
Now that we've written data to GCS, let's load it into BigQuery. In this section, we'll walk through the process of using Mage to load our data from GCS to BigQuery. This closely mirrors a very common data engineering workflow: loading data from a data lake into a data warehouse.
Videos
- 2.2.5a - Writing an ETL Pipeline : GCS to BigQuery
By now you're familiar with building pipelines, but what about adding parameters? In this video, we'll discuss some built-in runtime variables that exist in Mage and show you how to define your own! We'll also cover how to use these variables to parameterize your pipelines. Finally, we'll talk about what it means to backfill a pipeline and how to do it in Mage.
Videos
- 2.2.6a - Parameterized Execution
- 2.2.6b - Backfills
Resources
In this section, we'll cover deploying Mage using Terraform and Google Cloud. This section is optional— it's not necessary to learn Mage, but it might be helpful if you're interested in creating a fully deployed project. If you're using Mage in your final project, you'll need to deploy it to the cloud.
Videos
- 2.2.7a - Deployment Prerequisites
- 2.2.7b - Google Cloud Permissions
- 2.2.7c - Deploying to Google Cloud - Part 1
- 2.2.7d - Deploying to Google Cloud - Part 2
Resources
Additional Mage Guides
We've prepared a short exercise to test you on what you've learned this week. You can find the homework here. This follows closely from the contents of the course and shouldn't take more than an hour or two to complete. 😄
Congratulations! You've completed Week 2 of the Data Engineering Zoomcamp. We hope you've enjoyed learning about Mage and that you're excited to use it in your final project. If you have any questions, feel free to reach out to us on Slack. Be sure to check out our "Next Steps" video for some inspiration for the rest of your journey 😄.
Videos
- 2.2.9 - Next Steps
Resources
Did you take notes? You can share them here:
- 2024 Videos transcripts week 2 by Maria Fisher
- Notes from Jonah Oliver
- Notes from Linda
- Notes from Kirill
- Notes from Zharko
- Add your notes above this line
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