Training: Data Warehousing on AWS

Overview

Description

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 Objectives

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

Prerequisites

We recommend that attendees of this course have:

Building Data Lakes on AWS
Building Data Analytics Solutions Using Amazon Redshift
Familiarity with relational databases and database design concepts

Course duration / Price

3 days / € 2,685.00 (excl. tax) per person (DE)

Course outline

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 Redshiftmazon 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

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

The practical exercises are performed in prepared working environments available via web browser – no software needs to be installed. The course material is in English, spoken language can be in german or english. Other languages like spanish, portuguese or french, please contact us under training@tecracer.de

Highlights

Practice-oriented learning: Develop and manage data warehousing solutions using Amazon Redshift through extensive hands-on labs, covering data ingestion, transformation, orchestration, and machine learning integration.
Comprehensive expertise in data warehousing: Master Amazon Redshift features such as serverless architecture, SQL Query Editor v2, performance tuning, disaster recovery, and secure data management to design scalable and efficient data solutions.
Flexible and accessible training: Participate in browser-based practical exercises with multilingual course materials and instruction available in German, English, or other languages upon request.

Details

Sold by

tecRacer Germany GmbH

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Support

Vendor support

Support details This offer does not include a support package. Please contact training@tecracer.de if you have any questions.