Build Your First Serverless Data Engineering Project
Learn fundamental data engineering skills, get hands-on experience with AWS, and build a fully functional data pipeline for your resume.
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Course overview
Data is the lifeblood of the AI revolution.
Data powers everything from advanced analytics to data science, machine learning, large language models, and generative AI.
But data is useless until it can be harnessed.
Data engineering is about building systems to do exactly that. Data Engineers wrangle, manipulate, and transform data so it can be used for training AI, generating predictive analytics, and making critical business decisions. Without data engineering, none of this is even possible.
In this course, you'll take your first steps with data engineering. You'll build a serverless data pipeline and apply the core principles of all good data engineering work: efficiency, automation, and scalability.
You'll gain hands-on experience with:
- Serverless data ingestion
- Automated workflow orchestration for ETL (extract, transform, load)
- Advanced SQL strategies for transforming and wrangling data
- Visualizing data for analytics and business intelligence
- Creating and automating data quality checks
Throughout the course, you'll leverage the power of cloud technologies using Amazon Web Services (AWS), the most popular cloud environment.
To be ready to take this course, you should have some SQL and Python knowledge.
For a full breakdown of who the course is for and what you'll learn, check out the course schedule and syllabus below.
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If you are:
- A current college/grad student
- Outside the US
- Recently laid off
We still have some partial scholarships available for the upcoming April 7th, 2025 cohort. Please email dkfreitag@gmail.com to discuss.
Who this course is for
01
Data Analysts + Business Intelligence Analysts
You're looking to expand your skillset by learning Data Engineering and AWS skills.
02
Junior Data Engineers + Data Engineers
You've got some experience already, but you're new to AWS and looking to learn more.
03
This course is not for:
Experienced/Senior Data Engineers, AWS experts, or people with no previous coding experience.
What you’ll get out of this course
Build a fully functional data engineering project from start to finish
Build a project of your own to perform data engineering in a hands-on way.
Data engineering principles and frameworks
Gain proficiency in data engineering concepts like data ingestion, workflow orchestration, data transformation, and data quality testing.
Explore a variety of AWS services
Gain experience with many of the major AWS services used for serverless data engineering like Kinesis, Lambda, S3, Glue, and Athena.
Advanced SQL strategies for transforming data
Step-by-step examples of powerful SQL patterns for essential data engineering transform work.
Showcase your new knowledge
Learn effective ways to showcase your project to prospective employers and talk about it in future job interviews.
Personalized guidance every step of the way
New technologies can be challenging. As part of this live cohort course, you'll get personalized assistance when you need it.
What’s included
Live sessions
Learn directly from David Freitag in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
6 live sessions • 39 lessons
Week 1
- 🏁
Course Overview
- 💡
Data Engineering Principles
- 🔌
Download the course code repository
Apr
8
Session 1: Serverless Data Ingestion 101
Tue 4/812:00 AM—2:00 AM (UTC)- 🏁
Module Overview
- 💡
File storage in S3
- 💡
Importing data into Athena and querying it
- 💡
Lambda functions for ingesting data to S3
- 💡
EventBridge triggers to schedule/automate Lambdas
Apr
10
Session 2: Scaling and Automating Data Ingestion
Thu 4/1012:00 AM—2:00 AM (UTC)- 🏁
Module Overview
- 💡
Creating a Kinesis Firehose
- 💡
Pushing to the Firehose stream with Lambda
- 💡
Creating a Glue Crawler
- 💡
Data partitioning for cost and query efficiency
- 💡
Bonus: error alerting
Apr
13
Optional: Sunday Office Hours
Sun 4/138:00 PM—10:00 PM (UTC)- ✅
Week #1 project milestones
Course Introduction
Serverless Data Ingestion 101
S3, Athena, and Lambda Functions
Scaling and Automating Data Ingestion
Kinesis Firehose and Glue Crawlers
Project milestones for the week
Week 2
Apr
15
Session 3: Orchestrating Data Engineering Workflows
Tue 4/1512:00 AM—2:00 AM (UTC)- 🏁
Module Overview
- 💡
The Parquet data format and storage + query efficiency
- 💡
Creating jobs in Glue
- 💡
Accessing logs in CloudWatch
- 💡
Glue workflows
- 💡
Adding data quality checks to your pipeline
- 💡
Extra credit: event driven architecture
Apr
17
Session 4: Visualizing Data for Analytics and Business Intelligence
Thu 4/1712:00 AM—2:00 AM (UTC)- 🏁
Module Overview
- 💡
Creating a new user, adding permissions, and exporting credentials
- 💡
Connecting Grafana to Athena
- 💡
Writing queries to build visuals in Grafana
- 💡
Sharing your visualizations
Apr
18
Session 5: Showcasing Your Project
Fri 4/1812:00 AM—2:00 AM (UTC)- 🏁
Module Overview
- 💡
Hosting your project in a GitHub repository
- 💡
Describing your project on your resume
- 💡
Discussing your project in interviews
- ✅
Week #2 project milestones
- 📄
Project samples from previous students in this course
Orchestrating Data Engineering Workflows
Glue Jobs and ETL Workflows
Visualizing Data for Analytics and Business Intelligence
Using Grafana to visualize data
Showcasing Your Project
Build a GitHub portfolio, add it to your resume, and prep for interviews
Project milestones for the week
Bonus
- 🏁
Module Overview
- 💡
Grouping and aggregating data 101
- 💡
Creating new attributes with window functions
- 💡
CTE's, subqueries, and temp tables
- 💡
Mastering joins
- 💡
Fundamental data quality checks with SQL
SQL Strategies for Data Engineering
Bonus lessons on powerful SQL patterns for data engineering
What students are saying
Meet your instructor
David Freitag
David Freitag
David is a Senior Data Engineer at American Family Insurance, a top 10 auto insurance company in the Fortune 500.
David is a career changer who started his professional life as a high school teacher before pivoting to Data Engineering.
Over the last few years, he has also spent hundreds of hours tutoring students in Python, SQL, and Data Engineering skills.
If you'd like to learn these skills too, this course is for you.
Course schedule
6-8 hours per week
Main Course Sessions
8:00 PM - 10:00 (EDT)
Week 1: Monday, Wednesday, and Thursday
Week 2: Monday and Wednesday
After each lesson, there will be optional time to complete project work and get personalized assistance (office hours).
Office hours typically go until 10:30pm EDT (30 minutes).
Optional Sunday Office Hours
4:00 PM - 6:00 PM (EDT)
Get additional assistance with your project and ask questions during optional Sunday afternoon office hours.
Miss a class?
Lessons will be recorded
If you can't make a session, or you want to go back and rewatch a lesson, videos will be posted following each lesson.
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Get reimbursed by your employer
Your employer may have funds to cover this course
Many companies offer a professional development budget. Others may not have a formal policy but may still cover the cost. It's worth asking.
Make the ask with this pre-filled template
This email is pre-drafted to make it easy for you and your employer to understand how the course benefits your role.