Description
Duration: 1 day
About this Course
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.
Audience Profile
This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS
At Course Completion
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
Prerequisites
Students with a minimum one-year experience managing data warehouses will benefit from this course. We recommend that attendees of this course have
What’s included?
- Authorized Courseware
- Intensive Hands on Skills Development with an Experienced Subject Matter Expert
- Hands-on practice on real Servers and extended lab support 1.800.482.3172
- Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
- Academy Code of Honor: Test Pass Guarantee
- Optional: Package for Hotel Accommodations, Lunch and Transportation
With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.
Methods
- Instructor Led (the best training format we offer)
- Live Online Classroom – Online Instructor Led
- Self-Paced Video
Speak to an Admissions Representative for complete details
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
| Start | Finish | Public Price | Public Enroll | Private Price | Private Enroll |
|---|---|---|---|---|---|
| 9/23/2024 | 9/23/2024 | ||||
| 10/14/2024 | 10/14/2024 | ||||
| 11/4/2024 | 11/4/2024 | ||||
| 11/25/2024 | 11/25/2024 | ||||
| 12/16/2024 | 12/16/2024 | ||||
| 1/6/2025 | 1/6/2025 | ||||
| 1/27/2025 | 1/27/2025 | ||||
| 2/17/2025 | 2/17/2025 | ||||
| 3/10/2025 | 3/10/2025 | ||||
| 3/31/2025 | 3/31/2025 | ||||
| 4/21/2025 | 4/21/2025 | ||||
| 5/12/2025 | 5/12/2025 | ||||
| 6/2/2025 | 6/2/2025 | ||||
| 6/23/2025 | 6/23/2025 | ||||
| 7/14/2025 | 7/14/2025 | ||||
| 8/4/2025 | 8/4/2025 | ||||
| 8/25/2025 | 8/25/2025 | ||||
| 9/15/2025 | 9/15/2025 | ||||
| 10/6/2025 | 10/6/2025 | ||||
| 10/27/2025 | 10/27/2025 | ||||
| 11/17/2025 | 11/17/2025 | ||||
| 12/8/2025 | 12/8/2025 | ||||
| 12/29/2025 | 12/29/2025 | ||||
| 1/19/2026 | 1/19/2026 |
