Description

Duration

3 Days

18 CPD hours

This course is intended for

This course is intended for:
Database architects
Database administrators
Database developers
Data analysts and scientists

Overview

This course is designed to teach you how to:
Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions
Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud
Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution
Architect the data warehouse
Identify performance issues, optimize queries, and tune the database for better performance
Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket
Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse

Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data

Module 1: Introduction to Data Warehousing

Relational databases

Data warehousing concepts

The intersection of data warehousing and big data

Overview of data management in AWS

Hands-on lab 1: Introduction to Amazon Redshift

Module 2: Introduction to Amazon Redshift

Conceptual overview

Real-world use cases

Hands-on lab 2: Launching an Amazon Redshift cluster

Module 3: Launching clusters

Building the cluster

Connecting to the cluster

Controlling access

Database security

Load data

Hands-on lab 3: Optimizing database schemas

Module 4: Designing the database schema

Schemas and data types

Columnar compression

Data distribution styles

Data sorting methods

Module 5: Identifying data sources

Data sources overview

Amazon S3

Amazon DynamoDB

Amazon EMR

Amazon Kinesis Data Firehose

AWS Lambda Database Loader for Amazon Redshift

Hands-on lab 4: Loading real-time data into an Amazon Redshift database

Module 6: Loading data

Preparing Data

Loading data using COPY

Data Warehousing on AWS

AWS Classroom Training

Concurrent write operations

Troubleshooting load issues

Hands-on lab 5: Loading data with the COPY command

Module 7: Writing queries and tuning for performance

Amazon Redshift SQL

User-Defined Functions (UDFs)

Factors that affect query performance

The EXPLAIN command and query plans

Workload Management (WLM)

Hands-on lab 6: Configuring workload management

Module 8: Amazon Redshift Spectrum

Amazon Redshift Spectrum

Configuring data for Amazon Redshift Spectrum

Amazon Redshift Spectrum Queries

Hands-on lab 7: Using Amazon Redshift Spectrum

Module 9: Maintaining clusters

Audit logging

Performance monitoring

Events and notifications

Lab 8: Auditing and monitoring clusters

Resizing clusters

Backing up and restoring clusters

Resource tagging and limits and constraints

Hands-on lab 9: Backing up, restoring and resizing clusters

Module 10: Analyzing and visualizing data

Power of visualizations

Building dashboards

Amazon QuickSight editions and feature

Course Images

Data Warehousing on AWS

By Nexus Human

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