Introduction
This course dives deep into the Amazon Redshift service and the current thinking in building and operating data analytics pipelines to turn data into insights. You can ask questions and get real-time feedback from expert AWS instructors to immediately apply to your data analytics solutions. You’ll build a data warehouse analytics workflow based on a real-world scenario with the content learned through tutorials, hands-on labs, discussion, demonstration, presentations, and group exercises.
This course is part of the Building Modern Data Analytics Solutions on AWS collection of four, one-day intermediate-level classroom training courses.
Audience profile
This course is intended primarily for:
- Data warehouse engineers
- Data platform engineers
- Architects and operators who build and manage data analytics pipelines
Pre-requisites
It is recommend that attendees of this course have:
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS
Course objectives
This course is designed to teach you how 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 content
| Module A: Overview of Data Analytics and the Data Pipeline |
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- Using the data pipeline for analytics
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| Module A1: Using Amazon Redshift in the Data Analytics Pipeline |
- Why Amazon Redshift for data warehousing?
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- Overview of Amazon Redshift
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| Module A2: Introduction to Amazon Redshift |
- Amazon Redshift architecture
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- Interactive Demo 1: Touring the Amazon Redshift console
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- Practice Lab 1: Load and query data in an Amazon Redshift cluster
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| Module A3: Ingestion and Storage |
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- Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
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- Data distribution and storage
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- Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
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- Querying data in Amazon Redshift
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- Practice Lab 2: Data analytics using Amazon Redshift Spectrum
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| Module A4: Processing and Optimizing Data |
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- Practice Lab 3: Data transformation and querying in Amazon Redshift
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- Interactive Demo 4: Applying mixed workload management on Amazon Redshift
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- Automation and optimization
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- Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster
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| Module A5: Security and Monitoring of Amazon Redshift Clusters |
- Securing the Amazon Redshift cluster
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- Monitoring and troubleshooting Amazon Redshift clusters
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| Module A6: Designing Data Warehouse Analytics Solutions |
- Data warehouse use case review
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- Activity: Designing a data warehouse analytics workflow
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| Module B: Developing Modern Data Architectures on AWS |
- Data warehouse use case review
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| Module B1: Introduction to data lakes |
- Describe the value of data lakes
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- Compare data lakes and data warehouses
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- Describe the components of a data lake
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- Recognize common architectures built on data lakes
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| Module B2: Data ingestion, cataloguing, and preparation |
- Describe the relationship between data lake storage and data ingestion
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- Describe AWS Glue crawlers and how they are used to create a data catalogue
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- Identify data formatting, partitioning, and compression for efficient storage and query
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- Lab 1: Set up a simple data lake
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| Module B3: Data processing and analytics |
- Recognize how data processing applies to a data lake
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- Use AWS Glue to process data within a data lake
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- Describe how to use Amazon Athena to analyse data in a data lake
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| Module B4: Building a data lake with AWS Lake Formation |
- Describe the features and benefits of AWS Lake Formation
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- Use AWS Lake Formation to create a data lake
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- Understand the AWS Lake Formation security model
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- Lab 2: Build a data lake using AWS Lake Formation
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| Module B5: Additional Lake Formation configurations |
- Automate AWS Lake Formation using blueprints and workflows
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- Apply security and access controls to AWS Lake Formation
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- Match records with AWS Lake Formation FindMatches
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- Visualize data with Amazon QuickSight
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- Lab 3: Automate data lake creation using AWS Lake Formation blueprints
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- Lab 4: Data visualization using Amazon QuickSight
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| Module B6: Architecture and course review |
- Post course knowledge check
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Associated certifications and exam
On successful completion of this course students will receive a Torque IT attendance certificate.
