Amazon Redshift is a guided data warehouse that allows you to study all your data using standard SQL and existing Business Intelligence (BI) tools. Redshift put to use query optimization, columnar storage, parallel execution, and high-performance disks to query petabytes of data in seconds. Redshift is beneficial for companies that use SQL or existing BI tools and want to analyze large amounts of data with their existing tools. In the lab practice, you would be learning how to create, query, and resize a Redshift cluster.
What are the Prerequisites to learn Amazon Redshift Course?
*Having a basic understanding of the Linux bash shell
*Conceptual understanding of SQL and Redshift
What are the Training objectives to learn Amazon Redshift Training?
*Logging in to the AWS Management Console
*Using EC2 Instance Connect to communicate with Redshift
*Create and resize a Redshift cluster
*Load data into Redshift
*Query data with Redshift
What are the lab Steps to follow in Amazon Redshift training
* How to Log in to the Amazon Web Services Console
*Creating the Redshift Cluster
*Includes Connecting to the Virtual Machine using EC2 Instance
*Retrieving the Redshift IAM role
*Loading data into Redshift
*Running sample queries
*Resizing the cluster
*Cleaning up the environment
Amazon Redshift Online Training Course syllabus
Introduction to Amazon Web services
- Amazon Web Services Stack
- Introduction to Amazon DATABASE
Introduction to AWS Redshift
- AWS Redshift – Data Warehouse-as-a-Service
Walkthrough the AWS Management Console – Hands-On
Redshift Architecture Overview
- Leader and Compute Nodes
- Node Slices
- Columnar Storage for performance
- Economics of Redshift
- Key differentiators
- Common Use cases
Hands-on with AWS Redshift
- Launch a new Redshift Cluster
- Modifying a Cluster – resize, showdown, delete, and rebooting
- Security Groups.
- Parameter groups.
- Database Encryption.
- Backup recovery and creating a manual snapshot with automatic snapshots.
- Authorize access to Cluster
- Getting Information about Cluster Configuration.
- Database Audit Logging
Accessing Amazon Redshift cluster – Hands-On
- JDBC and ODBC interfaces
- Install and configure clients SQL tools by using JDBC and/or ODBC drivers
- How to Create Database, Users, user groups, permissions, and access controls in the environment
- Connect to Redshift Cluster
- Load sample data into the cluster
- Creating and testing queries against the data
Monitor cluster performance Hands-On
- Analyzing cluster Performance data
- Analyze query execution
- Working with performance metrics by creating alarm
Designing tables With Hands-On
- DDL SQL during Creating Tables, Alter tables, Drop tables.
- LIMITATIONS and what is implemented differently.
- Selecting distribution Style and distribution keys.
- Selecting Sort Key.
- To Choose the best Distribution key for covering all the use cases
- To Choose the best sort keys covering various use cases.
- Choosing a column compression type.
- Define constraints
Loading data While Hands-On
- Using Copy to Load data
- Loading data from S3
- Applying a Manifest to Specify Data Files
- Loading Compressed Files
- Loading Fixed-Width Data
- Loading Multi-byte Data
- Loading Encrypted Data Files
- Loading from JSON files.
- Insert, Select, Update, Delete
- Deep Copy
- LIMITATIONS Troubleshooting
- S3ServiceException Errors
- System Tables for Troubleshooting Data Loads
- Multi-byte Character Load Errors
- Error Reference
- Unloading Data to Amazon S3
- Unloading Encrypted Data Files
- Unloading Data in Fixed-Width Format
- Reloading Unloaded Data
Performance Tuning – Hands-On
- Query Processing
- Query Planning And Execution Workflow
- Reviewing Query Plan Steps
- Query Plan
- Factors Affecting Query Performance
- Analyzing and Improving Queries
- Query Analysis Workflow
- Reviewing Query Alerts
- Analyzing the Query Plan
- Analyzing the Query Summary
- Improving Query Performance
- Diagnostic Queries for Query Tuning
- Implementing Workload Management
- Defining Query Queues
- Modifying the WLM Configuration
- WLM Queue Assignment Rules
- Assigning Queries to Queues
- Dynamic and Static Properties
- Monitoring Workload Management
- To Configure WLM Queues to get Improve Query Processing
- Troubleshooting Queries
- How to Build Admin queries from system tables to support the performance.
- Migrating the Existing BI Systems to Redshift
- How to Load data from OLTP databases to Redshift to their Limitations.
- ETL and ELT on Redshift.
- To Build custom ETL and/or ELT framework from an OLTP db to Redshift
- Use of Powershell /Python AWS SDK’s for loading data from SQL Server/ORACLE/Postgres to Redshift through S3.
- Limitations of Best Practices for Redshift Data warehouse implementation.
Which Companies Use Amazon Redshift?
With regards to cloud computing services, Amazon Redshift is a powerhouse of data warehousing. It is used by some of the largest companies in the world, like Ford Motor Company, Left, Intuit, and Pfizer Plus etc. This data warehouse is used to store their cloud databases with the related production data. The idea of Big Data depends on a capacity to measure, store, and dissect information in huge secret stashes, and that is basically what Amazon Redshift gives.
Amazon Redshift has powered the advent of Big Data and data warehousing, allowing companies to build powerful applications and generate reports that provide all of the data they need to run a business.