Data Engineering using Databricks on AWS and Azure (Udemy.com)

As part of this course, you will learn all the Data Engineering using cloud platform-agnostic technology called Databricks.

About Data Engineering

Data Engineering is nothing but processing the data depending on our downstream needs. We need to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETL Development, Data Warehouse Development, etc.

About Databricks

Databricks is the most popular cloud platform-agnostic data engineering tech stack. They are the committers of the Apache Spark project. Databricks run time provide Spark leveraging the elasticity of the cloud. With Databricks, you pay for what you use. Over a period of time, they came up with the idea of Lakehouse by providing all the features that are required for traditional BI as well as AI & ML. Here are some of the core features of Databricks.

• Spark - Distributed Computing

• Delta Lake - Perform CRUD Operations. It is primarily used to build capabilities such as inserting, updating, and deleting the data from files in Data Lake.

• cloudFiles - Get the files in an incremental fashion in the most efficient way leveraging cloud features.

• Databricks SQL - A Photon-based interface that is fine-tuned for running queries submitted for reporting and visualization by reporting tools. It is also used for Ad-hoc Analysis.

Course Details

As part of this course, you will be learning Data Engineering using Databricks.

• Getting Started with Databricks

• Setup Local Development Environment to develop Data Engineering Applications using Databricks

• Using Databricks CLI to manage files, jobs, clusters, etc related to Data Engineering Applications

• Spark Application Development Cycle to build Data Engineering Applications

• Databricks Jobs and Clusters

• Deploy and Run Data Engineering Jobs on Databricks Job Clusters as Python Application

• Deploy and Run Data Engineering Jobs on Databricks Job Clusters using Notebooks

• Deep Dive into Delta Lake using Dataframes on Databricks Platform

• Deep Dive into Delta Lake using Spark SQL on Databricks Platform

• Building Data Engineering Pipelines using Spark Structured Streaming on Databricks Clusters

• Incremental File Processing using Spark Structured Streaming leveraging Databricks Auto Loader cloudFiles

• Overview of AutoLoader cloudFiles File Discovery Modes - Directory Listing and File Notifications

• Differences between Auto Loader cloudFiles File Discovery Modes - Directory Listing and File Notifications

• Differences between traditional Spark Structured Streaming and leveraging Databricks Auto Loader cloudFiles for incremental file processing.

• Overview of Databricks SQL for Data Analysis and reporting.

We will be adding a few more modules related to Pyspark, Spark with Scala, Spark SQL, and Streaming Pipelines in the coming weeks.

Desired Audience

Here is the desired audience for this advanced course.

• Experienced application developers to gain expertise related to Data Engineering with prior knowledge and experience of Spark.

• Experienced Data Engineers to gain enough skills to add Databricks to their profile.

• Testers to improve their testing capabilities related to Data Engineering applications using Databricks.

Prerequisites

• Logistics

  • Computer with decent configuration (At least 4 GB RAM, however 8 GB is highly desired)

  • Dual Core is required and Quad-Core is highly desired

  • Chrome Browser

  • High-Speed Internet

  • Valid AWS Account

  • Valid Databricks Account (free Databricks Account is not sufficient)

• Experience as Data Engineer especially using Apache Spark

• Knowledge about some of the cloud concepts such as storage, users, roles, etc.

Associated Costs

As part of the training, you will only get the material. You need to practice on your own or corporate cloud account and Databricks Account.

• You need to take care of the associated AWS or Azure costs.

• You need to take care of the associated Databricks costs.

Training Approach

Here are the details related to the training approach.

• It is self-paced with reference material, code snippets, and videos provided as part of Udemy.

• One needs to sign up for their own Databricks environment to practice all the core features of Databricks.

• We would recommend completing 2 modules every week by spending 4 to 5 hours per week.

• It is highly recommended to take care of all the tasks so that one can get real experience of Databricks.

• Support will be provided through Udemy Q&A.

Here is the detailed course outline.

Getting Started with Databricks on Azure

As part of this section, we will go through the details about signing up to Azure and setup the Databricks cluster on Azure.

• Getting Started with Databricks on Azure

• Signup for the Azure Account

• Login and Increase Quotas for regional vCPUs in Azure

• Create Azure Databricks Workspace

• Launching Azure Databricks Workspace or Cluster

• Quick Walkthrough of Azure Databricks UI

• Create Azure Databricks Single Node Cluster

• Upload Data using Azure Databricks UI

• Overview of Creating Notebook and Validating Files using Azure Databricks

• Develop Spark Application using Azure Databricks Notebook

• Validate Spark Jobs using Azure Databricks Notebook

• Export and Import of Azure Databricks Notebooks

• Terminating Azure Databricks Cluster and Deleting Configuration

• Delete Azure Databricks Workspace by deleting Resource Group

Azure Essentials for Databricks - Azure CLI

As part of this section, we will go through the details about setting up Azure CLI to manage Azure resources using relevant commands.

• Azure Essentials for Databricks - Azure CLI

• Azure CLI using Azure Portal Cloud Shell

• Getting Started with Azure CLI on Mac

• Getting Started with Azure CLI on Windows

• Warming up with Azure CLI - Overview

• Create Resource Group using Azure CLI