David Freitag

August 2024

May 2024

Self-paced cohort

December 2024

April 2025

Looking for a complete SQL reference guide with real, working example queries? Look no further. Enter your email below, and you'll get a free copy of this complete SQL cheat sheet. It covers everything from basic query syntax to filtering, ordering, unioning, joining, aggregating, and manipulating data. It also includes examples of more advanced SQL concepts like of CTE's, subqueries, CASE WHEN statements, and window functions.

The Complete SQL Cheat Sheet

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Learn fundamental data engineering skills, get hands-on experience with AWS, and build a fully functional data pipeline for your resume.

Build Your First Serverless Data Engineering Project

Sr. Pricing Systems Analyst

Data Analyst

Senior Manager

Data Engineer

Senior Data Integration Specialist

Backend developer | data Engineer | devOps

Senior Data Analytics Manager

Lead Data Acquisition Engineer

Insights & Analytics Lead, Lead Game Designer

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. --- 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.

Data is the lifeblood of the AI revolution. 

Course syllabus

Week 1: Monday, Wednesday, and ThursdayWeek 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).

Main Course Sessions

Get additional assistance with your project and ask questions during optional Sunday afternoon office hours.

Optional Sunday Office Hours

If you can't make a session, or you want to go back and rewatch a lesson, videos will be posted following each lesson.

Miss a class?

Course schedule

Data Analysts + Business Intelligence Analysts You're looking to expand your skillset by learning Data Engineering and AWS skills.

Junior Data Engineers + Data Engineers You've got some experience already, but you're new to AWS and looking to learn more.

This course is not for: Experienced/Senior Data Engineers, AWS experts, or people with no previous coding experience.

Who this course is for

What you’ll get out of this course

Build a project of your own to perform data engineering in a hands-on way.

Build a fully functional data engineering project from start to finish

Gain proficiency in data engineering concepts like data ingestion, workflow orchestration, data transformation, and data quality testing.

Data engineering principles and frameworks

Gain experience with many of the major AWS services used for serverless data engineering like Kinesis, Lambda, S3, Glue, and Athena.

Explore a variety of AWS services

Step-by-step examples of powerful SQL patterns for essential data engineering transform work.

Advanced SQL strategies for transforming data

Learn effective ways to showcase your project to prospective employers and talk about it in future job interviews.

Showcase your new knowledge

New technologies can be challenging. As part of this live cohort course, you'll get personalized assistance when you need it.

Learn directly from David Freitag

David Freitag

Courses

Build Your First Serverless Data Engineering Project

Lightning Lessons

SQL Toolkit for Data Engineering

SQL Toolkit for Data Engineering

SQL Toolkit for Data Engineering

SQL Toolkit for Data Engineering

Resources

The Complete SQL Cheat Sheet