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What you would learn in Mastering Amazon Redshift and Serverless for Data Engineers course?
AWS, also known as Amazon Redshift, is one of the essential AWS Services used to build Data Warehouses or Data Marts to provide reports and dashboards to business customers. In this course, you will finish understanding AWS and Amazon Redshift by exploring all of the essential capabilities that are available in AWS, as well as Amazon Redshift to create Data Warehouses or Data Marts.
Here is the complete outline of the course.
In the beginning, we'll learn how to get started with Amazon Redshift using AWS Web Console. We will look at how to set up an Amazon Redshift cluster, how you can join the cluster, and how to run queries with a web-based query editor. Also, we will set up the Database and tables for Redshift Cluster. Redshift Cluster. After we have created the database and tables, we'll examine the specifics concerning CRUD operations on tables in Databases within the Redshift Cluster.
After we have tables and databases within the Redshift Cluster, It is time to learn how we can get details into the tables within Redshift Cluster. One method we employ to transfer information into the Redshift cluster is copying data from s3 to Redshift tables. Using the Copy command, we will walk through the steps of copying data into the Redshift tables from S3.
Python is among the most popular programming languages for building Data Engineering or ETL Applications. It is widely used to create ETL jobs that convert information into Database Tables in the Redshift Cluster. Once we know how to transfer the data from s3 and into Redshift tables with the Copy Command, we will discover how to create using Python to create Data Engineering and ETL Applications using Redshift Cluster. We will discover how to execute CRUD-based operations and how to execute COPY Commands by using Python-based applications.
Once we have a better understanding of how to create applications using the Redshift Cluster, we will review some of the essential concepts used when developing Redshift Tables using Distkeys and Sortkeys.
We also can connect to remote databases like Postgres and perform queries directly on small tables of databases using Redshift Federated queries and perform queries over Glue and Athena Catalog using Redshift Spectrum. Learn how to utilize the power of Redshift Federated Queries and Spectrum to process data from distant Database Tables or in an s3 with no copying data.
Additionally, you will be given an introduction to Amazon Redshift Serverless as part of the Getting Started Guide.
Once you are familiar with Amazon Redshift Serverless, you will likely implement the Pipeline that includes a Spark Application installed on the AWS EMR Cluster that will load all the information processed by Spark in Redshift.
Content of the Course:
How to get started with Amazon Redshift using AWS Web Console
Copy data from s3 to AWS Redshift Tables by using Redshift Commands or Queries
Create applications with Redshift Cluster using Python as Programming Language
Copy data from s3 to AWS Redshift Tables by using Python as a programming Language
Create Tables by using databases set up using AWS Redshift Database Server with distribution Keys as well as Sort Keys
Use AWS Redshift Federated Queries connecting to RDBMS databases that are traditional such as Postgres
Perform ETL by using AWS Redshift Federated Queries with Redshift Capacity
Integration of AWS Redshift and AWS Glue Catalog to run queries with Redshift Spectrum
Use AWS Redshift Spectrum queries with Glue Catalog tables on Datalake set-up by using AWS S3
Beginning to Learn About Amazon Redshift Serverless by creating Workgroup and Namespace
Integration of AWS EMR Cluster with Amazon Redshift by using Serverless Workgroup
Develop and deploy the Spark application to AWS EMR Cluster in which the added data is loaded onto Amazon Redshift Serverless Workgroup
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