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Data Warehousing on AWS

  • Length 3 days
  • Price  $2860 inc GST
Course overview
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Why study this course

Get an introduction 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 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.

This course is delivered through a mix of instructor-led training (ILT) and hands-on labs.

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What you’ll learn

This course is designed to teach participants 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, optimise queries, and tune the database for better performance

  • Use Amazon Redshift Spectrum to analyse data directly from an Amazon S3 bucket

  • Use Amazon QuickSight to perform data analysis and visualisation tasks against the data warehouse

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AWS at Lumify Work

Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.

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Who is the course for?

This course is intended for:

  • Database architects

  • Database administrators

  • Database developers

  • Data analysts and scientists

Course subjects

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: Optimising 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

  • Maintaining tables

  • 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: Analysing and visualising data

  • Power of visualisations

  • Building dashboards

  • Amazon QuickSight editions and features

Please note: This is an emerging technology course. Course outline is subject to change as needed.

Prerequisites

It is recommended that attendees have the following prerequisites:

  • AWS Technical Essentials course or equivalent experience

  • Familiarity with relational databases and database design concepts

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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