Training: AWS – Data Warehousing on Amazon Web Services

Ref. AWS-302

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Duration:

3

days

Exam:

Optional

Level:

Intermediate

Funding:

Eligible

Home > Trainings > IT Pro > Cloud Computing > AWS – Data Warehousing on Amazon Web Services

Description

This 3-day course introduces you to concepts, strategies, and best practices for designing a cloudbased 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 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.

Participant Profiles

Database Architects
Database Administrators
Database Developers
Data Analysts
Data Scientists

Objectives

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

Prerequisites

Familiarity with relational databases and database design concepts
Completed the AWS Technical Essentials classroom training or have equivalent experience

Course Content

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

Module 2: Introduction to Amazon Redshift

Conceptual overview
Real-world use cases

Module 3: Launching clusters

Building the cluster
Connecting to the cluster
Controlling access
Database security
Load data

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

Module 6: Loading data

Preparing Data
Loading data using COPY
Maintaining tables
Concurrent write operations
Troubleshooting load issues

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)

Module 8: Amazon Redshift Spectrum

Amazon Redshift Spectrum
Configuring data for Amazon Redshift Spectrum
Amazon Redshift Spectrum Queries

Module 9: Maintaining clusters

Audit logging
Performance monitoring
Events and notifications
Resizing clusters
Backing up and restoring clusters
Resource tagging and limits and constraints

Module 10: Analyzing and visualizing data

Power of visualizations
Building dashboards
Amazon QuickSight editions and features

Documentation

Digital courseware included

Lab / Exercises

Official AWS Labs

Complementary Courses

Eligible Funding

ITTA is a partner of a continuing education fund dedicated to temporary workers. This fund can subsidize your training, provided that you are subject to the “Service Provision” collective labor agreement (CCT) and meet certain conditions, including having worked at least 88 hours in the past 12 months.