Need help with Data-engineering-nanodegree?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

186 Stars 154 Forks MIT License 43 Commits 0 Opened issues


Projects done in the Data Engineering Nanodegree by

Services available


Need anything else?

Contributors list


Projects done in the Data Engineering Nanodegree by


Course 1: Data Modeling

Introduction to Data Modeling

➔ Understand the purpose of data modeling

➔ Identify the strengths and weaknesses of different types of databases and data storage techniques

➔ Create a table in Postgres and Apache Cassandra

Relational Data Models

➔ Understand when to use a relational database

➔ Understand the difference between OLAP and OLTP databases

➔ Create normalized data tables

➔ Implement denormalized schemas (e.g. STAR, Snowflake)

NoSQL Data Models

➔ Understand when to use NoSQL databases and how they differ from relational databases

➔ Select the appropriate primary key and clustering columns for a given use case

➔ Create a NoSQL database in Apache Cassandra

Project: Data Modeling with Postgres and Apache Cassandra

Course 2: Cloud Data Warehouses

Introduction to the Data Warehouses

➔ Understand Data Warehousing architecture

➔ Run an ETL process to denormalize a database (3NF to Star)

➔ Create an OLAP cube from facts and dimensions

➔ Compare columnar vs. row oriented approaches

Introduction to the Cloud with AWS

➔ Understand cloud computing

➔ Create an AWS account and understand their services

➔ Set up Amazon S3, IAM, VPC, EC2, RDS PostgreSQL

Implementing Data Warehouses on AWS

➔ Identify components of the Redshift architecture

➔ Run ETL process to extract data from S3 into Redshift

➔ Set up AWS infrastructure using Infrastructure as Code (IaC)

➔ Design an optimized table by selecting the appropriate distribution style and sorting key

Project 2: Data Infrastructure on the Cloud

Course 3: Data Lakes with Spark

The Power of Spark

➔ Understand the big data ecosystem

➔ Understand when to use Spark and when not to use it

Data Wrangling with Spark

➔ Manipulate data with SparkSQL and Spark Dataframes

➔ Use Spark for ETL purposes

Debugging and Optimization

➔ Troubleshoot common errors and optimize their code using the Spark WebUI

Introduction to Data Lakes

➔ Understand the purpose and evolution of data lakes

➔ Implement data lakes on Amazon S3, EMR, Athena, and Amazon Glue

➔ Use Spark to run ELT processes and analytics on data of diverse sources, structures, and vintages

➔ Understand the components and issues of data lakes

Project 3: Big Data with Spark

Course 4: Automate Data Pipelines

Data Pipelines

➔ Create data pipelines with Apache Airflow

➔ Set up task dependencies

➔ Create data connections using hooks

Data Quality

➔ Track data lineage

➔ Set up data pipeline schedules

➔ Partition data to optimize pipelines

➔ Write tests to ensure data quality

➔ Backfill data

Production Data Pipelines

➔ Build reusable and maintainable pipelines

➔ Build your own Apache Airflow plugins

➔ Implement subDAGs

➔ Set up task boundaries

➔ Monitor data pipelines

Project: Data Pipelines with Airflow

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.