Learn Data Engineering With Databricks On Aws Cloud

Build Data Engineering Pipelines on AWS using Databricks core features such as Spark and Delta Lake

What you'll learn
Data Engineering leveraging Databricks features
Databricks CLI to manage files, Data Engineering jobs and clusters for Data Engineering Pipelines
Deploying Data Engineering applications developed using PySpark on job clusters.
Deploying Data Engineering applications developed using PySpark using Notebooks on job clusters
Building Data Engineering Pipelines using Spark Structured Streaming on Databricks Clusters
Setting up development environment to develop Data Engineering applications using Databricks.

Requirements
Must have Prior programming experience in SQL and Python.
Experience in Spark Programming is a must.
Prior basic experience or understanding of cloud services like AWS is important.

Description
Get ready to Learn Data Engineering with Databricks on AWS Cloud with this complete course. Gain familiarity with the course details and topics designed to help you succeed.This comprehensive course is designed to equip you with the skills and knowledge needed to excel in the field of data engineering using two powerful platforms: Databricks and Amazon Web Services (AWS). Data engineering is the backbone of any successful data-driven initiative, and Databricks, a unified analytics platform, has emerged as a leading choice for data engineers and data scientists worldwide. When combined with AWS, a cloud computing powerhouse, you have a robust ecosystem that can handle data at scale, provide advanced analytics capabilities, and support a wide range of data sources and formats.Learn about Data Engineering with Databricks on AWS with Hands-On LabsLearn Data Engineering with Databricks on AWS Cloud is a hands-on practice course designed to familiarize you with the core functionality of Databricks by connecting it with AWS to perform Data Engineering. Through hands-on exercises, you'll gain a thorough understanding of Databrick's architecture and how it revolutionizes data engineering in the cloud. You'll explore the seamless integration of Databricks with AWS services, such as Amazon S3 and Glue, unlocking a world of possibilities for managing and analyzing your data.This course has been meticulously designed to provide you with both a solid theoretical foundation and extensive hands-on practice in the dynamic realms of data engineering, Databricks, and Amazon Web Services (AWS).The course comprises approximately 50 labs starting from the basics and moving to high levels in terms of complexity.Who should take this course?The course "Learn Data Engineering with Databricks on AWS Cloud" is designed for a wide range of individuals who are interested in building expertise in data engineering using Databricks on the AWS Cloud. If you're looking to start a career in data engineering, this course is an excellent choice. It will provide you with the foundational knowledge and practical skills needed to become a successful data engineer. Data scientists and analysts who want to expand their skill set and be able to work with large-scale data processing, data pipelines, and data lakes can greatly benefit from this course. IT professionals who want to transition into roles focused on data engineering and cloud computing can use this course as a stepping stone to acquire the necessary skills and knowledge. Individuals interested in cloud computing, specifically AWS, and its applications in data engineering will gain a deep understanding of cloud-based data engineering solutions.Requirements● Basic knowledge of SQL or writing queries in any language● Scripting in Python Willingness to explore, learn, and put in the extra effort to succeed● An active AWS Account & know-how of basic cloud fundamentals● Programming experience using Python● Data Engineering experience using Spark

Overview
Section 1: Getting Startted with Databricks on AWS

Lecture 1 Introduction to Getting Started with Databricks on AWS

Lecture 2 Signing up for aws free account

Lecture 3 Logging in into AWS Management Console

Lecture 4 Setting up Databricks workspace on AWS using Quickstart

Lecture 5 Logging in into Databricks Workspace on AWS

Lecture 6 Cleaning up the workspace and resources

Lecture 7 Quick Walkthrough of Databricks UI on AWS

Lecture 8 Creating Single-Node Databricks cluster on AWS

Lecture 9 Upload Data using AWS Databricks UI

Lecture 10 Develop spark Application using AWS Databricks Notebook

Lecture 11 Writing dataframe to DBFS

Lecture 12 Export and Import AWS Databricks Notebooks

Section 2: AWS Storage Solutions

Lecture 13 Getting Started with AWS S3

Lecture 14 Overview of AWS S3 Glacier

Lecture 15 Creating S3 Bucket and adding Objects

Lecture 16 Version Control in AWS S3

Lecture 17 AWS S3 Cross-Region Replication for Fault Tolerance

Lecture 18 Setup and configure AWS S3 CLI using IAM user credentials

Lecture 19 Managing Objects in AWS S3 using CLI

Section 3: AWS S3 and IAM Role

Lecture 20 Overview of IAM for Databricks on AWS

Lecture 21 Creating AWS IAM User

Lecture 22 Logging into AWS Management Console using IAM User

Lecture 23 Validate Programmatic Access to AWS IAM User3

Lecture 24 AWS IAM Identity-based policies

Lecture 25 AWS IAM User Groups

Lecture 26 AWS IAM Custom Policies

Section 4: Integration S3 and Glue Catalog

Lecture 27 Introduction to Integrating AWS s3 and Glue Catalog with Databricks

Lecture 28 Create AWS IAM Group for Databricks Developers

Lecture 29 Creating AWS IAM Users and adding to group

Lecture 30 Creating AWS s3 Bucket for Databricks Developers

Lecture 31 Grant Permissions on AWS S3 Bucket to the users in group

Lecture 32 Attach AWS IAM Policy to grant access to Glue

Lecture 33 Upload JSON Dataset to s3 to crawl using AWS Glue Crawler

Lecture 34 Create AWS IAM Custom Service Role for Glue Crawlers

Lecture 35 Create and Run Glue Crawler to Create Multiple Glue Catalog Tables

Lecture 36 Overview of Integration of Databricks Clusters and AWS EC2 Instances

Lecture 37 Create AWS IAM Role or Instance Profile

Lecture 38 Registering AWS IAM Instance Profile with Databricks Account

Lecture 39 Attach AWS IAM Instance Profile to Databricks Cluster

Lecture 40 Grant Permissions on S3 to Databricks Clusters

Lecture 41 Integrate Databricks Cluster with Glue Catalog via Instance Profile

Section 5: Setup local development environment for databricks

Lecture 42 Setup single node databricks cluster

Lecture 43 Install Databricks Connect

Lecture 44 Configure Databricks Connect

Section 6: Using Databricks CLI

Lecture 45 Install and configure databricks CLI

Lecture 46 Interacting with File System using Databricks CLI

Lecture 47 Getting Cluster details using Databricks CLI

Section 7: Spark Jobs Deployment using Notebooks

Lecture 48 Modularizing Notebooks

Lecture 49 Running Job using Notebook

Lecture 50 Refactor application as Databricks Notebooks

Lecture 51 Run Notebooks using Development Cluster

Software engineers, aspiring data engineers or data analyst & data scientists,Programmers and Database Administrators with experience in writing SQL queries,BI Analysts looking to enhance their understanding of data engineering, particularly in the context of big data and cloud platforms, can leverage this course to broaden their skill set.,IT professionals who want to transition into roles focused on data engineering and cloud computing

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