Path overview
In this path, you’ll master the mandatory technical skills for modern data engineering, including Python programming, distributed computing, containerization, and cloud deployment. You’ll learn how to work with production databases like PostgreSQL, Snowflake, and MongoDB, process data at scale with PySpark, orchestrate workflows with Apache Airflow, and deploy containerized applications to cloud platforms using Docker and Kubernetes.
Best of all, you’ll learn by doing – you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Key skills
- Programming with Python and building complex data architecture to support organizations' data strategy
- Managing distributed data processing with PySpark and orchestrating workflows with Apache Airflow
- Containerizing applications with Docker and deploying them to cloud infrastructure
- Building production-grade data pipelines that scale automatically and run reliably in cloud environments
Path outline
Part 1: Introduction to Python [4 courses]
Part 2: Introduction to Algorithms [1 course]
Part 3: The Command Line and Git [4 courses]
Part 4: Working with Data Sources Using SQL [5 courses]
Part 5: Production Databases [3 courses]
Part 6: Python for Large Datasets [5 courses]
Part 7: Distributed Data Processing [2 courses]
Part 8: Containerization and Infrastructure [2 courses]
Part 9: Pipeline Orchestration and Cloud Deployment [3 courses]
Projects in this path
Learn and Install Jupyter Notebook
For this project, you’ll take on the role of a Jupyter Notebook beginner. You’ll learn the essentials of running code, adding explanatory text, and installing Jupyter locally to prepare for real-world data projects.
Profitable App Profiles for the App Store and Google Play Markets
For this project, we’ll assume the role of data analysts for a company that builds free Android and iOS apps. Our revenue depends on in-app ads, so our goal is to analyze data to determine which kinds of apps attract more users.
Exploring Hacker News Posts
For this project, we’ll step into the role of data analysts to explore Hacker News submissions, analyzing trends using skills in string manipulation, object-oriented programming, and date handling in Python.
Building Fast Queries on a CSV
For this project, we’ll step into the role of Python developers to build an inventory system for a laptop store. We’ll apply efficient data structures and algorithms to enable fast queries.
Analyzing Kickstarter Projects
For this project, you’ll assume the role of a data analyst at a startup considering launching a Kickstarter campaign. You’ll analyze data to help the team understand what might influence a campaign’s success.
Plus 9 more projects
Build your project portfolio with the Data Engineer path.
The Dataquest guarantee
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.
We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.
Master skills faster with Dataquest
Go from zero to job-ready
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build your project portfolio
Build confidence with our in-depth projects, and show off your data skills.
Challenge yourself with exercises
Work with real data from day one with interactive lessons and hands-on exercises.
Showcase your path certification
Share the evidence of your hard work with your network and potential employers.
Grow your career with
Dataquest.
98%
of learners recommend
Dataquest for career advancement
4.85
Dataquest rating
SwitchUp Best Bootcamps
$30k
Average salary boost
for learners who complete a path
Join 1M+ data learners on
Dataquest.
1
Create a free account
2
Choose a learning path
3
Complete exercises and projects
4