# Building Streaming Data Analytics Solutions on AWS

## Price

**$900.00 (AUD)**  
**$900.00 (NZD)**

## Duration

**1 Day**

## Modality

**Live – Virtual Instructor Led**

## Course code

**AWS-DATA-REDSHIFT**

## Course Overview

In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.

## Course Objectives

In this course, you will learn to:

- Compare the features and benefits of data warehouses, data lakes, and modern data architectures
- Design and implement a data warehouse analytics solution
- Identify and apply appropriate techniques, including compression, to optimize data storage
- Select and deploy appropriate options to ingest, transform, and store data
- Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
- Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
- Secure data at rest and in transit
- Monitor analytics workloads to identify and remediate problems & Apply cost management best practices

### Target Audience

This course is intended for:

- Data warehouse engineers
- Data platform engineers
- Architects and operators who build and manage data analytics pipelines

### Prerequisites

Students with a minimum one-year experience managing data warehouses will benefit from this course.

We recommend that attendees of this course have:

- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS

## Module Breakdown

#### Module A

Overview of Data Analytics and the Data Pipeline

- Data analytics use cases
- Using the data pipeline for analytics

#### Module 1

Using Amazon Redshift in the Data Analytics Pipeline

- Why Amazon Redshift for data warehousing?
- Overview of Amazon Redshift

#### Module 2

Introduction to Amazon Redshift

- Amazon Redshift architecture
- **Interactive Demo 1**: Touring the Amazon Redshift console
- Amazon Redshift features
- Practice Lab 1: Load and query data in an Amazon Redshift cluster

#### Module 3

Ingestion and Storage

- Ingestion
- **Interactive Demo 2:** Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
- Data distribution and storage
- **Interactive Demo 3:** Analyzing semi-structured data using the SUPER data type
- Querying data in Amazon Redshift
- **Practice Lab 2:** Data analytics using Amazon Redshift Spectrum

#### Module 4

Processing and Optimizing Data

- Data transformation
- Advanced querying
- **Practice Lab 3:** Data transformation and querying in Amazon Redshift
- Resource management
- **Interactive Demo 4:** Applying mixed workload management on Amazon Redshift
- Automation and optimization
- **Interactive demo 5**: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster

#### Module 5

Security and Monitoring of Amazon Redshift Clusters

- Securing the Amazon Redshift cluster
- Monitoring and troubleshooting Amazon Redshift clusters

#### Module 6

Designing Data Warehouse Analytics Solutions

- Data warehouse use case review
- **Activity**: Designing a data warehouse analytics workflow

## Class Schedule

| Date                  | Start Time | Location  |  |
| --------------------- | ---------- | --------- | -- |
| Monday 16th October   | 9:00am    | New Zealand | [Register Now](https://bespoketraining.my.salesforce-sites.com/attendee?Id=a00Mn0000063KwPIAU) |