Rating 4.48 out of 5 (243 ratings in Udemy)
What you'll learn- Redshift - Core Components and Features
- Loading and Querying Data - Query Editor, SQL Client Tools, BI Tools, CLI, APIs
- Automated Table Design, Data Distribution, Sorting and Workload Management
- Cluster Resizing, Relocation, Pause, Shutdown, Backup
- Security, Logging and Monitoring in Redshift
- Redshift Spectrum - Querying the Datalake
- Federated Queries - Querying RDS (relational database)
- Redshift ML - Advanced analytics …
Rating 4.48 out of 5 (243 ratings in Udemy)
What you'll learn- Redshift - Core Components and Features
- Loading and Querying Data - Query Editor, SQL Client Tools, BI Tools, CLI, APIs
- Automated Table Design, Data Distribution, Sorting and Workload Management
- Cluster Resizing, Relocation, Pause, Shutdown, Backup
- Security, Logging and Monitoring in Redshift
- Redshift Spectrum - Querying the Datalake
- Federated Queries - Querying RDS (relational database)
- Redshift ML - Advanced analytics with Redshift & Sagemaker
- Stored Procedures, Materialized Views
DescriptionAmazon or AWS Redshift is a fast, easy to use, cost-effective, peta-byte level, cloud data warehousing solution. In this course we will learn multiple features of Redshift in a step-by-step approach. This tutorial is well supported by hands-on lab exercises and the set of Interview Questions.
The course is divided in following core areas
Overview - Brief introduction to AWSRedshift, Datawarehouse Systems &evolution of related technologies
Getting Started - We will get started on Amazon Redshift by loading and querying sample data. We will understand the bare minimum of redshift ecosystem including Redshift Console, Query Editor and Configurations needed to integrate an external SQLClient - DBeaver.
How does it work - We will jump into the core mechanics of Redshift where we will understand the core components, functions of redshift including Redshift Cluster, Nodes, Distribution Key, Sort Key, Results Cache, AQUA, RA3 Instances, etc.
Manage Data - We will move to manage data section where we will understand the redshift data types, data loading & data querying options, automated table design, stored procedures, materialized views and workload management.
AWS Services - In this section, we will understand some advanced features of Redshift including Redshift Spectrum, Redshift ML and Federated Queries. We will also understand the integration aspects of redshift with other AWS services such as data-lake formation, aws EMR, AWS glue, Amazon Kinesis, Amazon Quicksight, AWSSagemaker, etc.
Manage Cluster - In this section, we will understand the management aspects of redshift including the basic cluster operations, snapshots as backups, monitoring, logging & data security.
Conclude - In the last section, we will understand the redshift cost and pricing, best practices, reading references, etc.
