Most of the projects on Amazon cloud would host their data either in DynamoDB or RDS, but would eventually ship all their data in Redshift or EMR for analytics. Redshift is also a heavy-weight topic in AWS Certified Big Data Specialty exam.
Amazon Redshift is a managed data warehouse that allows you to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. Redshift uses query optimization, columnar storage, parallel execution, and high-performance disks to query petabytes of data in seconds. Redshift is valuable for companies that use SQL or existing BI tools and want to analyze large amounts of data with their existing tools. In this Lab, you will learn how to create, query, and resize a Redshift cluster.
Course content
Introduction to Amazon Web services
- Amazon Web Services Stack
- Introduction to Amazon DATABASE
Introduction to AWS Redshift
- AWS Redshift – Data Warehouse-as-a-Service
- Features
- Pricing
- What Is A Data Warehouse?
- Shortcomings Of Self-Owned Data Warehouses
- Benefits Of A Public Cloud Data Warehouse
- Benefits Of Redshift
- How Companies Use Redshift
- Third Party Visualization Tools Overview
- How Redshift Compares To Other Analytics Tools
RedShift Architecture:
- Data Loading
- Data Distribution Concepts
- Basic Redshift Usage
- Clusters
- Leader and Compute Nodes
- Node Slices
- Columnar Storage for performance
- Economics of Redshift
- Key differentiators
- Common Use cases
- Lab: Creating An AWS Account
- Lab: Creating A Redshift Cluster
- Lab: Configuring SQL Workbench
- Lab: Loading Data Into S3
- Lab: Loading Data Into A Redshift Cluster
- Lab: Querying A Redshift Cluster
RedShift DDL Commands:
- Data Types and Operators in Amazon Redshift
- Lab – Data Types and Operators in Amazon Redshift
- CREATE Objects with DDL commands in Amazon Redshift
- MODIFY Objects with DDL commands in Amazon Redshift
- DELETE Objects with DDL commands in Amazon Redshift
- Lab: – DDL Statement in Amazon Redshift
RedShift DML Commands:
- Loading Data in Amazon Redshift
- Lab – Loading Data in Amazon Redshift
- Lab – Unloading Or Downloading Data from Amazon Redshift
- SQL Statements for CRUD Operations in Amazon Redshift
- SQL Statements for Transactions in Amazon Redshift
- SQL Statements for System Statistics in Amazon Redshift
- Lab – SQL Statements for Administering Privileges in Amazon Redshift
- Built-in Functions, Expressions and Operators in Amazon Redshift
Unloading Data
- Unloading Data to Amazon S3
- Unloading Encrypted Data Files
- Unloading Data in Delimited or Fixed-Width Format
- Reloading Unloaded Data
RedShift Advanced Table Design
- Amazon Redshift Table Design – Constraints
- Table Design Resources
- Lab: Amazon Redshift Table Design – Compression Encodings
- Lab: Amazon Redshift Table Design – Distribution Styles
- Lab: Amazon Redshift Table Design – Sort Keys
Redshift System Information:
- System and Catalog Tables in Amazon Redshift
- System Views in Amazon Redshift
Performance Tuning & Optimizing Queries
- Query Performance Factors in Amazon Redshift
- How to Diagnose Query Performance in Amazon Redshift
- Operations & Workload Management
- Lab: Query Planning and Execution Workflow in Amazon Redshift
- Lab: Incremental data loads in Amazon Redshift
- Parameter Groups in Amazon Redshift
- Workload Management in Amazon Redshift
- Lab: Parameters Groups and Workload Management in Amazon Redshift
RedShift Cluster Administration:
- Amazon Redshift Limits
- Lab: Configuration and Status Overview of Amazon Redshift Dashboard
- Lab: Advanced Network Configuration of Amazon Redshift Cluster
- Lab: Resource Tagging in Amazon Redshift Cluster
- Lab: Events and Subscriptions in Amazon Redshift Cluster
- Lab: Auditing and Logging in Amazon Redshift Cluster
- Lab: Backups and Disaster Recovery in Amazon Redshift
- Lab: Backups and Disaster Recovery in Amazon Redshift Cluster
- Lab: Performance Monitoring of Amazon Redshift Cluster
- Lab: Developing Cluster Management Dashboards for Amazon Redshift Cluster
- Lab: Resizing Amazon Redshift Cluster
- Cost Optimization of Amazon Redshift Cluster
- Lab: Data Encryption in Amazon Redshift Cluster
- Amazon Redshift Best Practices
Advanced Topics:
- Data Loading Best Practices
- Tuning Query Performance
- Lab: Connecting Tableau
- Lab: Tableau Usage
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Course Prerequisites
You should be familiar with:
- Basic understanding of the Linux bash shell
- Conceptual understanding of SQL and Redshift
- Basic knowledge of database and data warehouse concepts
- Basic knowledge of AWS Concepts like VPC, S3, and EC2
- Course includes demo of all the labs. An AWS Account would be required to try labs hands-on.
- Basic working knowledge of any SQL style query language
- No prior knowledge of Amazon Redshift is required
Who can attend
- Anyone who wants to learn Redshift and secure a job role like Analyst / Developer / Architect / Administrator can take this course
- Students who are pursuing AWS Big Data Specialty exam can take this course
Number of Hours: 30hrs
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