Length
3 days

Overview

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.

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

Key Topics

Detailed Info
  • Introduction to Data Warehousing
  • Understanding Amazon Redshift Components and Resources
  • Choosing a Data Warehousing Approach
  • Identifying Data Sources and Requirements
  • Architecting the Data Warehouse
  • Loading Data into the Data Warehouse
  • Optimising Queries and Tuning Performance
  • Maintaining, Monitoring and Auditing the Data Warehouse
  • Analysing and Visualising Data
Skills Gained
Key Topics
Target Audience
Prerequisites

Skills Gained

In this course, participants will learn how to:

  • Discuss the core concepts of data warehousing
  • Discuss 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
  • Evaluate approaches and methodologies for designing data warehouses
  • Identify data sources and determine requirements for accessing the data
  • Architect the data warehouse
  • Use important commands, such as COPY, UNLOAD, and VACUUM, to manage the data in 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 features and services, such as Amazon Redshift database auditing, Amazon CloudWatch, Amazon CloudTrail, and Amazon Simple Notification Service (Amazon SNS), to monitor and audit the data warehouse
  • Use Amazon QuickSight to perform data analysis and visualisation tasks against the data warehouse

Key Topics

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.

Target Audience

This course is intended for:

  • Database architects
  • Database administrators
  • Database developers
  • Data analysts and scientists

We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please call us at +632 8244 2098 or email [email protected].

Prerequisites

We recommend that attendees of this course have the following prerequisites:

  • AWS Technical Essentials course or equivalent experience
  • Familiarity with relational databases and database design concepts
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The supply of this course by DDLS 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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