Building Data Analytics Solutions Using Amazon Redshift

Mastering Advanced Techniques for Scalable Data Warehousing and Analytics with AWS

1 day

English

In Kooperation mit

In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service.

Content

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.

Outline

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

Module B: Developing Modern Data Architectures on AWS

Modern data architecture

Benefits

Comparing the features and benefits of data warehouses, data lakes, and modern data architectures
Designing and implementing a data warehouse analytics solution
Identifying and applying appropriate techniques, including compression, to optimize data storage
Selecting and deploying appropriate options to ingest, transform, and store data
Choosing the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
Understanding how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
Securing data at rest and in transit
Monitoring analytics workloads to identify and remediate problems
Applying cost management best practices

IMPORTANT: This course prepares you for AWS Data Analytics Certification, among other courses of the Data Analytics job role track.

Instructor

Yuri Nikulin

Matthew Millward

Methods

This course includes presentations, interactive demos, practice labs, discussions, and class exercises.

Who should attend

This course is intended for the following job roles:

Data Analytics

Attending the following course or equivalent knowledge is required: Building Data Lakes on AWS