Data Engineering with Microsoft Azure

Name: Data Engineering with Microsoft Azure Nanodegree Program
Rating: 4.3 (2 reviews)

Nanodegree Program

Learn to design data models, build data warehouses, build data lakes and lakehouse architecture, create data pipelines, and work with large datasets on the Azure platform using Azure Synapse Analytics, Azure Databricks, and Azure Data Factory.

Advanced

4 months

Real-world Projects

Completion Certificate

Last Updated May 16, 2024

Skills you'll learn:

Database fundamentals • Cassandradb • PostgreSQL • Database normalization

Prerequisites:

Command line interface basics • Relational data models • Intermediate Python

Courses In This Program

Course 1 • 45 minutes

Welcome to the Nanodegree Program!

Welcome to Udacity! We're excited to share more about your Nanodegree program and start this journey with you!

Lesson 1

Welcome!

Welcome to Udacity. Takes 5 minutes to get familiar with Udacity courses and gain some tips to succeed in courses.

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Lesson 1

Welcome!

Welcome to Udacity. Takes 5 minutes to get familiar with Udacity courses and gain some tips to succeed in courses.

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Course 2 • 4 weeks

Data Modeling

Learn to create relational and NoSQL data models to fit the diverse needs of data consumers. Use ETL to build databases in PostgreSQL and Apache Cassandra.

Lesson 1

Introduction to Data Modeling

In this lesson, students will learn the basic difference between relational and non-relational databases, and how each type of database fits the diverse needs of data consumers.

Lesson 2

Relational Data Models

In this lesson, students understand the purpose of data modeling, the strengths and weaknesses of relational databases, and create schemas and tables in Postgres

Lesson 3

NoSQL Data Models

Students will understand when to use non-relational databases based on the data business needs, their strengths and weaknesses, and how to creates tables in Apache Cassandra.

Lesson 4 • Project

Project: Data Modeling with Apache Cassandra

Students will model event data to create a non-relational database and ETL pipeline for a music streaming app. They will define queries and tables for a database built using Apache Cassandra.

Lesson 1

Introduction to Data Modeling

In this lesson, students will learn the basic difference between relational and non-relational databases, and how each type of database fits the diverse needs of data consumers.

Lesson 2

Relational Data Models

In this lesson, students understand the purpose of data modeling, the strengths and weaknesses of relational databases, and create schemas and tables in Postgres

Lesson 3

NoSQL Data Models

Students will understand when to use non-relational databases based on the data business needs, their strengths and weaknesses, and how to creates tables in Apache Cassandra.

Lesson 4 • Project

Project: Data Modeling with Apache Cassandra

Students will model event data to create a non-relational database and ETL pipeline for a music streaming app. They will define queries and tables for a database built using Apache Cassandra.

Course 3 • 4 weeks

Cloud Data Warehouses with Azure

In this course, you will learn to create cloud-based data warehouses and sharpen your data warehousing skills, deepen your knowledge of data infrastructure, and be introduced to data engineering on the cloud using Azure.

Lesson 1

Introduction to Cloud Data Warehouses with Azure

In this lesson, you'll learn about the course, including the prerequisites, tools, environment, and course project.

Lesson 2

Introduction to Data Warehouses

In this lesson, you'll be introduced to data warehouses, ETL, and OLAP cubes.

Lesson 3

ELT and Data Warehouse Technology in the Cloud

In this lesson, you'll learn about ELT, the differences between ETL and ELT, and general cloud data warehouse technologies.

Lesson 4

Azure Data Warehouse Technologies

In this lesson, you will learn about specific data warehouse technologies and solutions in Azure.

Lesson 5

Implementing Data Warehouses in Azure

In this lesson, you will have the opportunity to implement a data warehouse in Azure using Synapse.

Lesson 6 • Project

Building an Azure Data Warehouse for Bike Share Data Analytics

In this project, you will develop a data warehouse solution using Azure Synapse Analytics to analyze bike share data.

Lesson 1

Introduction to Cloud Data Warehouses with Azure

In this lesson, you'll learn about the course, including the prerequisites, tools, environment, and course project.

Lesson 2

Introduction to Data Warehouses

In this lesson, you'll be introduced to data warehouses, ETL, and OLAP cubes.

Lesson 3

ELT and Data Warehouse Technology in the Cloud

In this lesson, you'll learn about ELT, the differences between ETL and ELT, and general cloud data warehouse technologies.

Lesson 4

Azure Data Warehouse Technologies

In this lesson, you will learn about specific data warehouse technologies and solutions in Azure.

Lesson 5

Implementing Data Warehouses in Azure

In this lesson, you will have the opportunity to implement a data warehouse in Azure using Synapse.

Lesson 6 • Project

Building an Azure Data Warehouse for Bike Share Data Analytics

In this project, you will develop a data warehouse solution using Azure Synapse Analytics to analyze bike share data.

Course 4 • 4 weeks

Data lakes and Lakehouses with Spark and Azure Databricks

Learn about the big data ecosystem and how to use Spark to work with massive datasets. Learners will also store big data in a data lake and develop Lakehouse architecture on the Azure Databricks platform.

Lesson 1

Course Introduction

In this lesson, you'll learn about the course, including the prerequisites, tools, environment, and course project.

Lesson 2

Big Data Ecosystem, Data Lakes, and Spark

In this lesson, you will learn about the problems that Apache Spark is designed to solve. You'll also learn about the greater Big Data ecosystem and how Spark fits into it.

Lesson 3

Data Wrangling with Spark

In this lesson, we'll dive into how to use Spark for cleaning and aggregating data.

Lesson 4

Spark Debugging and Optimization

In this lesson, you will learn best practices for debugging and optimizing your Spark applications.

Lesson 5

Azure Databricks

In this lesson, you'll create Spark Clusters and Spark code on the Azure Databricks platform.

Lesson 6

Data Lakes and Lakehouse with Azure Databricks

In this lesson, you'll create data lakes and Lakehouse architecture on the Azure Databricks platform

Lesson 7 • Project

Building an Azure Data Lake for Bike Share Data Analytics

In this project, you'll implement Lakehouse architecture on the Azure Databricks platform.

Lesson 1

Course Introduction

In this lesson, you'll learn about the course, including the prerequisites, tools, environment, and course project.

Lesson 2

Big Data Ecosystem, Data Lakes, and Spark

In this lesson, you will learn about the problems that Apache Spark is designed to solve. You'll also learn about the greater Big Data ecosystem and how Spark fits into it.

Lesson 3

Data Wrangling with Spark

In this lesson, we'll dive into how to use Spark for cleaning and aggregating data.

Lesson 4

Spark Debugging and Optimization

In this lesson, you will learn best practices for debugging and optimizing your Spark applications.

Lesson 5

Azure Databricks

In this lesson, you'll create Spark Clusters and Spark code on the Azure Databricks platform.

Lesson 6

Data Lakes and Lakehouse with Azure Databricks

In this lesson, you'll create data lakes and Lakehouse architecture on the Azure Databricks platform

Lesson 7 • Project

Building an Azure Data Lake for Bike Share Data Analytics

In this project, you'll implement Lakehouse architecture on the Azure Databricks platform.

Taught By The Best

Vishnu (Lucky) Pamula

Sr. Cloud Solution Architect at Microsoft

Lucky is a data & AI evangelist with a track record of successfully helping organizations build analytics solutions. Besides his day job, he teaches as an adjunct professor, delivers lunch & learns, mentors students, and evangelizes Azure Quantum as an ambassador.

Amanda Moran

Developer Advocate at DataStax

Amanda is a developer advocate for DataStax after spending the last 6 years as a software engineer on 4 different distributed databases. Her passion is bridging the gap between customers and engineering. She has degrees from the University of Washington and Santa Clara University.

Matt Swaffer, PhD

Data Science Practice Lead at Cognitell

Matt is a data science professional whose career has spanned software development, user experience design, and data visualization. He earned his PhD in the research area of cognitive psychology in human learning and is an adjunct professor teaching software design courses.

Ratings & Reviews

Average Rating: 4.3 Stars

2 Reviews

Alvaro F.

November 29, 2022

It went really well. It's easy to follow the steps and lear. The experience with the platform could be better though.

Obinna Ezinwa O.

October 22, 2022

the videos are easy to understand. project review helps to clarify questions

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