Mastering Amazon Redshift and Serverless for Data Engineers

Mastering Amazon Redshift and Serverless for Data Engineers, available at $74.99, has an average rating of 4.31, with 208 lectures, based on 237 reviews, and has 7750 subscribers.

You will learn about Getting Started with Amazon Redshift using AWS Web Console Copy Data from s3 into AWS Redshift Tables using Redshift Queries or Commands Develop Applications using Redshift Cluster using Python as Programming Language Copy Data from s3 into AWS Redshift Tables using Python as Programming Language Create Tables using Databases setup on AWS Redshift Database Server using Distribution Keys and Sort Keys Run AWS Redshift Federated Queries connecting to traditional RDBMS Databases such as Postgres Perform ETL using AWS Redshift Federated Queries using Redshift Capacity Integration of AWS Redshift and AWS Glue Catalog to run queries using Redshift Spectrum Run AWS Redshift Spectrum Queries using Glue Catalog Tables on Datalake setup using AWS s3 Getting Started with Amazon Redshift Serverless by creating Workgroup and Namespace Integration of AWS EMR Cluster with Amazon Redshift using Serverless Workgroup Develop and Deploy Spark Application on AWS EMR Cluster where the processed data will be loaded into Amazon Redshift Serverless Workgroup This course is ideal for individuals who are University Students who want to learn AWS Redshift for Data Warehousing or Aspiring Data Engineers and Data Scientists who want to learn about AWS Redshift for Data Warehousing or Experienced Application Developers who would like to explore AWS Redshift for Data Warehousing or Experienced Data Engineers to build end to end data pipelines using Python around Data Marts created using AWS Redshift or Any IT Professional who is keen to deep dive into AWS Redshift for Data Warehousing on AWS It is particularly useful for University Students who want to learn AWS Redshift for Data Warehousing or Aspiring Data Engineers and Data Scientists who want to learn about AWS Redshift for Data Warehousing or Experienced Application Developers who would like to explore AWS Redshift for Data Warehousing or Experienced Data Engineers to build end to end data pipelines using Python around Data Marts created using AWS Redshift or Any IT Professional who is keen to deep dive into AWS Redshift for Data Warehousing on AWS.

Enroll now: Mastering Amazon Redshift and Serverless for Data Engineers

Summary

Title: Mastering Amazon Redshift and Serverless for Data Engineers

Price: $74.99

Average Rating: 4.31

Number of Lectures: 208

Number of Published Lectures: 208

Number of Curriculum Items: 208

Number of Published Curriculum Objects: 208

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Getting Started with Amazon Redshift using AWS Web Console

Copy Data from s3 into AWS Redshift Tables using Redshift Queries or Commands

Develop Applications using Redshift Cluster using Python as Programming Language

Copy Data from s3 into AWS Redshift Tables using Python as Programming Language

Create Tables using Databases setup on AWS Redshift Database Server using Distribution Keys and Sort Keys

Run AWS Redshift Federated Queries connecting to traditional RDBMS Databases such as Postgres

Perform ETL using AWS Redshift Federated Queries using Redshift Capacity

Integration of AWS Redshift and AWS Glue Catalog to run queries using Redshift Spectrum

Run AWS Redshift Spectrum Queries using Glue Catalog Tables on Datalake setup using AWS s3

Getting Started with Amazon Redshift Serverless by creating Workgroup and Namespace

Integration of AWS EMR Cluster with Amazon Redshift using Serverless Workgroup

Develop and Deploy Spark Application on AWS EMR Cluster where the processed data will be loaded into Amazon Redshift Serverless Workgroup

Who Should Attend

University Students who want to learn AWS Redshift for Data Warehousing

Aspiring Data Engineers and Data Scientists who want to learn about AWS Redshift for Data Warehousing

Experienced Application Developers who would like to explore AWS Redshift for Data Warehousing

Experienced Data Engineers to build end to end data pipelines using Python around Data Marts created using AWS Redshift

Any IT Professional who is keen to deep dive into AWS Redshift for Data Warehousing on AWS

Target Audiences

University Students who want to learn AWS Redshift for Data Warehousing

Aspiring Data Engineers and Data Scientists who want to learn about AWS Redshift for Data Warehousing

Experienced Application Developers who would like to explore AWS Redshift for Data Warehousing

Experienced Data Engineers to build end to end data pipelines using Python around Data Marts created using AWS Redshift

Any IT Professional who is keen to deep dive into AWS Redshift for Data Warehousing on AWS

AWS or Amazon Redshift is one of the key AWS Services used in building Data Warehouses or Data Marts to serve reports and dashboards for business users. As part of this course, you will end up learning AWS or Amazon Redshift by going through all the important features of AWS or Amazon Redshift to build Data Warehouses or Data Marts.

We have covered features such as Federated Queries, Redshift Spectrum, Integration with Python, AWS Lambda Functions, Integration of Redshift with EMR, and End-to-End Pipelineusing AWS Step Functions.

Here is the detailed outline of the course.

First, we will understand how to Get Started with Amazon Redshift using AWS Web Console. We will see how to create a cluster, how to connect to the cluster, and also how to run the queries using a Web-based query editor. We will also go ahead and create a Database and tables in the Redshift Cluster. Once we set up a Database and tables, we will also go through the details related to CRUD Operations against tables in Databases in Redshift Cluster.

Once we have the databases and tables in Redshift Cluster, it is time for us to understand how to get data into the tables in Redshift Cluster. One of the common approaches we use to get data into the Redshift cluster is by Copying Data from s3 into Redshift Tables. We will go through the step-by-step process of copying the data into Redshift tables from s3 using the copy command.

Python is one of the prominent programming languages to build Data Engineering or ETL Applications. It is extensively used to build ETL Jobs to get data into Database Tables in Redshift Cluster. Once we understand how to get data from s3 to Redshift tables using Copy Command, we will learn how to Develop Python-based Data Engineering or ETL Applications using Redshift Cluster. We will learn how to perform CRUD operations and also how to take run COPY Commands using Python-based programs.

Once we understand how to build applications using Redshift Cluster, we will go through some of the key concepts used while creating Redshift Tables with Distkeys and Sortkeys.

We can also connect to remote databases such as Postgres and run queries directly on the remote database tables using Redshift Federated Queries and also we can run queries on top of Glue or Athena Catalog using Redshift Spectrum. You will learn how to leverage Redshift Federated Queries and Spectrum to process data in remote Database tables or s3 without copying the data.

You will also get an overview of Amazon Redshift Serverless as part of Getting Started with Amazon Redshift Serverless.

Once you learn Amazon Redshift Serverless, you will end up deploying a Pipeline where a Spark Application is deployed on AWS EMR Cluster which will load the data processed by Spark into Redshift.

Course Curriculum

Chapter 1: Introduction to Mastering Amazon Redshift and Serverless for Data Engineers

Lecture 1: Introduction to Mastering Amazon Redshift and Serverless for Data Engineers

Chapter 2: Getting Started with Amazon Redshift

Lecture 1: Getting Started with Amazon Redshift – Introduction

Lecture 2: Create Redshift Cluster using Free Trial

Lecture 3: Connecting to Database using Redshift Query Editor

Lecture 4: Get list of tables querying information schema

Lecture 5: Run Queries against Redshift Tables using Query Editor

Lecture 6: Create Redshift Table using Primary Key

Lecture 7: Insert Data into Redshift Tables

Lecture 8: Update Data in Redshift Tables

Lecture 9: Delete data from Redshift tables

Lecture 10: Redshift Saved Queries using Query Editor

Lecture 11: Deleting Redshift Cluster

Lecture 12: Restore Redshift Cluster from Snapshot

Chapter 3: Copy Data from s3 into Redshift Tables

Lecture 1: Copy Data from s3 to Redshift – Introduction

Lecture 2: Setup Data in s3 for Redshift Copy

Lecture 3: Create Database and Table for Redshift Copy Command

Lecture 4: Create IAM User with full access on s3 for Redshift Copy

Lecture 5: Run Copy Command to copy data from s3 to Redshift Table

Lecture 6: Troubleshoot Errors related to Redshift Copy Command

Lecture 7: Run Copy Command to copy from s3 to Redshift table

Lecture 8: Validate using queries against Redshift Table

Lecture 9: Overview of Redshift Copy Command

Lecture 10: Create IAM Role for Redshift to access s3

Lecture 11: Copy Data from s3 to Redshift table using IAM Role

Lecture 12: Setup JSON Dataset in s3 for Redshift Copy Command

Lecture 13: Copy JSON Data from s3 to Redshift table using IAM Role

Chapter 4: Develop Applications using Redshift Cluster

Lecture 1: Develop application using Redshift Cluster – Introduction

Lecture 2: Allocate Elastic Ip for Redshift Cluster

Lecture 3: Enable Public Accessibility for Redshift Cluster

Lecture 4: Update Inbound Rules in Security Group to access Redshift Cluster

Lecture 5: Create Database and User in Redshift Cluster

Lecture 6: Connect to database in Redshift using psql

Lecture 7: Change Owner on Redshift Tables

Lecture 8: Download Redshift JDBC Jar file

Lecture 9: Connect to Redshift Databases using IDEs such as SQL Workbench

Lecture 10: Setup Python Virtual Environment for Redshift

Lecture 11: Run Simple Query against Redshift Database Table using Python

Lecture 12: Truncate Redshift Table using Python

Lecture 13: Create IAM User to copy from s3 to Redshift Tables

Lecture 14: Validate Access of IAM User using Boto3

Lecture 15: Run Redshift Copy Command using Python

Chapter 5: Redshift Tables with Distkeys and Sortkeys

Lecture 1: Redshift Tables with Distkeys and Sortkeys – Introduction

Lecture 2: Quick Review of Redshift Architecture

Lecture 3: Create multi-node Redshift Cluster

Lecture 4: Connect to Redshift Cluster using Query Editor

Lecture 5: Create Redshift Database

Lecture 6: Create Redshift Database User

Lecture 7: Create Redshift Database Schema

Lecture 8: Default Distribution Style of Redshift Table

Lecture 9: Grant Select Permissions on Catalog to Redshift Database User

Lecture 10: Update Search Path to query Redshift system tables

Lecture 11: Validate table with DISTSTYLE AUTO

Lecture 12: Create Cluster from Snapshot to the original state

Lecture 13: Overview of Node Slices in Redshift Cluster

Lecture 14: Overview of Distribution Styles

Lecture 15: Distribution Strategies for retail tables in Redshift

Lecture 16: Create Redshift tables with distribution style all

Lecture 17: Troubleshoot and Fix Load or Copy Errors

Lecture 18: Create Redshift Table with Distribution Style Auto

Lecture 19: Create Redshift Tables using Distribution Style Key

Lecture 20: Delete Cluster with manual snapshot

Chapter 6: Redshift Federated Queries and Spectrum

Lecture 1: Redshift Federated Queries and Spectrum – Introduction

Lecture 2: Overview of integrating RDS and Redshift for Federated Queries

Lecture 3: Create IAM Role for Redshift Cluster

Lecture 4: Setup Postgres Database Server for Redshift Federated Queries

Lecture 5: Create tables in Postgres Database for Redshift Federated Queries

Lecture 6: Creating Secret using Secrets Manager for Postgres Database

Lecture 7: Accessing Secret Details using Python Boto3

Lecture 8: Reading Json Data to Dataframe using Pandas

Lecture 9: Write JSON Data to Database Tables using Pandas

Lecture 10: Create IAM Policy for Secret and associate with Redshift Role

Lecture 11: Create Redshift Cluster using IAM Role with permissions on secret

Lecture 12: Create Redshift External Schema to Postgres Database

Lecture 13: Update Redshift Cluster Network Settings for Federated Queries

Lecture 14: Performing ETL using Redshift Federated Queries

Lecture 15: Clean up resources added for Redshift Federated Queries

Lecture 16: Grant Access on Glue Data Catalog to Redshift Cluster for Spectrum

Lecture 17: Setup Redshift Clusters to run queries using Spectrum

Lecture 18: Quick Recap of Glue Catalog Database and Tables for Redshift Spectrum

Lecture 19: Create External Schema using Redshift Spectrum

Lecture 20: Run Queries using Redshift Spectrum

Lecture 21: Cleanup the Redshift Cluster

Chapter 7: Getting Started with Amazon Serverless Redshift

Lecture 1: Create Workgroup and Namespace for Amazon Redshift Serverless

Lecture 2: Overview of Amazon Redshift Serverless Namespaces and Workgroups

Lecture 3: Quick Preview of Amazon Redshift Serverless Dashboard

Lecture 4: Validate Amazon Redshift Serverless Workgroup by running a query

Lecture 5: Enable Public Accessbility to Redshift Serverless Workgroup

Lecture 6: Understand Redshift Serverless Workgroup Capacity measured in RPUs

Chapter 8: Setup Redshift Spectrum Schema using Redshift Serverless

Lecture 1: Introduction to Setup Redshift Spectrum Database using Redshift Serverless

Lecture 2: Setup Files in S3 for Glue Catalog and Redshift Spectrum Database Tables

Lecture 3: Cleanup Glue Catalog Database and Crawler using AWS Glue Console

Lecture 4: Create Glue Crawler to Setup Glue Catalog Database and Tables for Redshift Shift

Instructors

Durga Viswanatha Raju Gadiraju
CEO at ITVersity and CTO at Analytiqs, Inc

Pratik Kumar

Sathvika Dandu

Madhuri Gadiraju

Sai Varma

Phani Bhushan Bozzam

Rating Distribution

1 stars: 3 votes

2 stars: 8 votes

3 stars: 23 votes

4 stars: 76 votes

5 stars: 127 votes

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