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Building Data Analytics Solutions Using Amazon Redshift
Duration
1 Day
Course
AWS-285
Available Formats
Classroom Training, Online Training
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Course Description
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 level: Intermediate
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
Audience
This course is intended for data warehouse engineers, data platform engineers, and 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
Topics
Module A: Overview of Data Analytics and the Data Pipeline
- Data analytics use cases
- Using the data pipeline for analytics
- Why Amazon Redshift for data warehousing?
- Overview of 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
- 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
- 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
- Securing the Amazon Redshift cluster
- Monitoring and troubleshooting Amazon Redshift clusters
- Data warehouse use case review
- Activity: Designing a data warehouse analytics workflow
- Modern data architectures
Related Courses
-
Data Warehousing on AWS
AWS-165- Duration: 3 Days
- Delivery Format: Classroom Training, Online Training
- Price: 2,025.00 USD
-
Building Data Lakes on AWS
AWS-255- Duration: 1
- Delivery Format: Classroom Training, Online Training
- Price: 675.00 USD
Self-Paced Training Info
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.