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Enjoy the best in Building Batch Data Analytics Solutions Using Amazon Redshift Training

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.

Building Batch Data Analytics Solutions Using Amazon Redshift Training

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.

Intended Audience

This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.

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

Course 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 architectures

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

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