Azure Databricks Course Syllabus

A hands-on course to learn how to use the Microsoft Azure Spark-based data analytics platform

Goals

• Know the concepts and features of Azure Databricks and its integration with other Azure services.
• Learn how to create and configure Spark clusters in Azure Databricks and run jobs and notebooks.
• Use Delta Lake to manage and optimize the storage and processing of structured and semi-structured data in Azure Databricks.
• Use SQL Warehouses to create and query scalable, distributed data warehouses in Azure Databricks.
• Integrate Azure Databricks with Azure Data Factory to automate and orchestrate data analysis workflows.

Content

• Unit 1: Explore Azure Databricks
• What is Azure Databricks and how it works
• Azure Databricks benefits and use cases
• Azure Databricks architecture and components
• How to create and access an Azure Databricks workspace
• Unit 2: Spark on Azure Databricks
• What is Spark and how it works
• Spark concepts and terminology
• How to create and manage Spark clusters in Azure Databricks
• How to run Spark jobs and notebooks in Azure Databricks
• How to use Spark APIs (PySpark, Spark SQL, SparkR, etc.) in Azure Databricks
• Unit 3: Use Delta Lake on Azure Databricks
• What is Delta Lake and how it works
• Delta Lake Benefits and Use Cases
• How to create and read Delta Lake tables in Azure Databricks
• How to use Delta Lake write and update operations in Azure Databricks
• How to optimize Delta Lake performance and reliability on Azure Databricks
• Unit 4: Use SQL Warehouses in Azure Databricks
• What is SQL Warehouses and how does it work?
• SQL Warehouses Benefits and Use Cases
• How to create and configure SQL Warehouses in Azure Databricks
• How to load and query data in SQL Warehouses in Azure Databricks
• How to integrate SQL Warehouses with other Azure services
• Unit 5: Run Azure Databricks Notebooks with Azure Data Factory
• What is Azure Data Factory and how it works
• Azure Data Factory benefits and use cases
• How to create and configure an Azure Data Factory
• How to create and run Azure Data Factory pipelines
• How to integrate Azure Databricks with Azure Data Factory

Introduction

Azure Databricks is a Spark-based data analytics platform that offers a simplified user experience and native integration with other Azure services. With Azure Databricks, you can quickly and easily create and run Spark clusters, and take advantage of Spark APIs to process and analyze large volumes of data from various sources and formats. Additionally, you can use Delta Lake to improve the quality and performance of your data, SQL Warehouses to create and query scalable, distributed data warehouses, and Azure Data Factory to automate and orchestrate data analysis workflows.

This course is designed for you to learn how to use Azure Databricks in a practical and efficient way. Throughout the course, you will carry out exercises and projects that will allow you to apply the concepts and techniques explained in each unit. Upon completion of the course, you will be able to use Azure Databricks to create robust and scalable data analytics solutions in the Azure cloud.

Addressed to

This course is intended for data professionals, analysts, data scientists, data engineers, and developers who want to learn how to use Azure Databricks to build and run Spark-based data analytics solutions. Previous knowledge of Spark, SQL, and Python, as well as Azure and cloud basics, is recommended.

Other aspects:

• Requirements: To take this course, you will need an Azure subscription, a web browser, and a code editor. It is also recommended to have Anaconda or Jupyter Notebook installed to work with Spark notebooks.
• Evaluation: The course includes quizzes, exercises and projects that will allow you to evaluate your learning and put into practice what you have learned. At the end of the course, you will receive a certificate of completion if you have successfully completed all activities.
• Resources: The course provides you with the resources you need to follow the content and complete the activities, such as Spark notebooks, sample data, reference guides, and links of interest.
• Duration: The course has an estimated duration of 20 hours, which can be distributed according to your pace and availability. It is recommended to dedicate at least one hour a day to the course to get the most out of it.