Welcome to the Data Engineering Nanodegree Program

Introduction to Data Engineering

Introduction to Data Modeling

In this course, you’ll learn to create relational and NoSQL data models to fit the diverse needs of data consumers. You’ll understand the differences between different data models, and how to choose the appropriate data model for a given situation. You’ll also build fluency in PostgreSQL and Apache Cassandra

Relational Data Models

Project Data Modeling with Postgres

NoSQL Data Models

Project Data Modeling with Apache Cassandra

Introduction to Data Warehouses

In this course, you’ll learn to create cloud-based data warehouses. You’ll sharpen your data warehousing skills, deepen your understanding of data infrastructure, and be introduced to data engineering on the cloud using Amazon Web Services (AWS).

Introduction to Cloud Computing and AWS

Implementing Data Warehouses on AWS

Project: Data Warehouse

The Power of Spark

In this course, you will learn more about the big data ecosystem and how to use Spark to work with massive datasets. You’ll also learn about how to store big data in a data lake and query it with Spark.

Data Wrangling with Spark

Debugging and Optimization

Introduction to Data Lakes

Project: Data Lake

Data Pipeline

In this course, you’ll learn to schedule, automate, and monitor data pipelines using Apache Airflow. You’ll learn to run data quality checks, track data lineage, and work with data pipelines in production.

Data Quality

Production Data Pipelines

Project Data Pipelines

Take 30 Min to Improve your LinkedIn

Capstone Project

Job Search

You’re in this Nanodegree program to take the next big step in your career - maybe you’re looking for a new job, or you’re learning new skills for your current job … or maybe you’re not sure what to do, but you know you need to make a career change.

Refine Your Entry-Level Resume

Craft Your Cover Letter

Optimize Your GitHub Profile

Develop Your Personal Brand


Project: Data Modeling with Postgres


A startup called Sparkify wants to analyze the data they’ve been collecting on songs and user activity on their new music streaming app. The analytics team is particularly interested in understanding what songs users are listening to. Currently, they don’t have an easy way to query their data, which resides in a directory of JSON logs on user activity on the app, as well as a directory with JSON metadata on the songs in their app.

They’d like a data engineer to create a Postgres database with tables designed to optimize queries on song play analysis, and bring you on the project. Your role is to create a database schema and ETL pipeline for this analysis. You’ll be able to test your database and ETL pipeline by running queries given to you by the analytics team from Sparkify and compare your results with their expected results.

Project Description

In this project, you’ll apply what you’ve learned on data modeling with Postgres and build an ETL pipeline using Python. To complete the project, you will need to define fact and dimension tables for a star schema for a particular analytic focus, and write an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL.