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Data Warehousing on AWS

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Trainingsbeschrijving

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Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift. This course demonstrates how to ingest, store, and transform data in the data warehouse. Topics covered include: the purpose of Amazon Redshift, how Amazon Redshift addresses business and technical challenges, features and capabilities of Amazon Redshift, designing a Data Warehousing Solution on AWS by applying best practices based on the Well-Architected Framework, integration with AWS and non-AWS products and services, performance tuning, orchestration, and securing and monitoring Amazon Redshift.

Course level: Advanced

Duration: 3 days


Activities

This course includes presentations, hands-on labs, and demonstrations.

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Doelgroep

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This course is intended for:

- Data engineers

- Data architects

- Database architects

- Database administrators

- Database developers

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Trainingsdoelstellingen

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In this course, you will learn to:

  • Describe Amazon Redshift architecture and its roles in a modern data architecture
  • Design and implement a data warehouse in the cloud using Amazon Redshift
  • Identify and load data into an Amazon Redshift data warehouse from a variety of sources
  • Analyze data using SQL QEV2 notebooks
  • Design and implement a disaster recovery strategy for an Amazon Redshift data warehouse
  • Perform maintenance and performance tuning on an Amazon Redshift data warehouse
  • Secure and manage access to an Amazon Redshift data warehouse
  • Share data between multiple Redshift clusters in an organization
  • Orchestrate workflows in the data warehouse using AWS Step Functions state machines
  • Create an ML model and configure predictors using Amazon Redshift ML
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Inhoud training

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Day 1

Module 1: Data Warehouse Concepts

  • Modern data architecture
  • Introduction to the course story
  • Data warehousing with Amazon Redshift
  • Amazon Redshift Serverless architecture
  • Hands-On Lab: Launch and Configure an Amazon Redshift Serverless Data Warehouse

Module 2: Setting up Amazon Redshift

  • Data models for Amazon Redshift
  • Data management in Amazon Redshift
  • Managing permissions in Amazon Redshift
  • Hands-On Lab: Setting up a Data Warehouse using Amazon Redshift Serverless

Module 3: Loading Data

  • Overview of data sources
  • Loading data from Amazon Simple Storage Service (Amazon S3)
  • Extract, transform, and load (ETL) and extract, load, and transform (ELT)
  • Loading streaming data
  • Loading data from relational databases
  • Hands-On Lab: Populating the data warehouse

Day 2

Module 4: Deep Dive into SQL Query Editor v2 and Notebooks

  • Features of Amazon Redshift Query Editor v2
  • Demonstration: Using Amazon Redshift Query Editor v2
  • Advanced queries
  • Hands-On Lab: Data Wrangling on AWS

Module 5: Backup and Recovery

  • Disaster recovery
  • Backing up and restoring Amazon Redshift provisioned
  • Backing up and restoring Amazon Redshift Serverless

Module 6: Amazon Redshift Performance Tuning

  • Factors that impact query performance
  • Table maintenance and materialized views
  • Query analysis
  • Workload management
  • Tuning guidance
  • Amazon Redshift monitoring
  • Hands-On Lab: Performance Tuning the Data Warehouse

Module 7: Securing Amazon Redshift

  • Introduction to Amazon Redshift security and compliance
  • Authentication with Amazon Redshift
  • Access control with Amazon Redshift
  • Data encryption with Amazon Redshift
  • Auditing and compliance with Amazon Redshift
  • Hands-On Lab: Securing Amazon Redshift

Day 3

Module 8: Orchestration

  • Overview of data orchestration
  • Orchestration with AWS Step Functions
  • Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)
  • Hands-On Lab: Orchestrating the Data Warehouse Pipeline

Module 9: Amazon Redshift ML

  • Machine Learning Overview
  • Getting started with Amazon Redshift ML
  • Amazon Redshift ML workflow scenarios
  • Amazon Redshift ML Usage
  • Hands-On Lab: Predicting customer churn with Amazon Redshift ML

Module 10: Amazon Redshift Data Sharing

  • Overview of data sharing in Amazon Redshift
  • Amazon DataZone for Data as a service

Module 11: Wrap-Up

  • Hands-On Lab: End of course challenge lab
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Voorkennis

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We recommend that attendees of this course have completed the following courses:

  • Fundamentals of Analytics on AWS – Part 1 (Digital course)
  • Fundamentals of Analytics on AWS – Part 2 (Digital course)
  • Building Data Lakes on AWS (Instructor led Training)
  • Building Data Analytics Solutions Using Amazon Redshift (Instructor led Training)
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