Data Warehousing on AWS

Data Warehousing on AWS GK4375

Kurskode: GK4375

varighet: 3 Dag(er)

Sted: Virtual, Instructor Led Training

Katergori: Amazon Web Services

Course Overview

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.

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

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

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)

This course is intended for:
- Data engineers
- Data architects
- Database architects
- Database administrators
- Database developers

NOK 31.000

Dato

Nullstill

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Kurskode: GK4375 Kateegori: Amazon Web Services, Cloud Computing

Salar Ghadani

Salg

Har du spørsmål, ta kontakt

918 34 713 salar@sgpartner.no

Kurskode: GK4375

varighet: 3 Dag(er)

KATEGORI: Amazon Web Services

Underkatergori: Cloud Computing

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