What you'll learn
Understand the fundamental concepts of data warehousing, columnar storage, and Massively Parallel Processing (MPP) architecture in Amazon Redshift.
Launch, configure, and connect to an Amazon Redshift cluster in AWS, including choosing node types, configuring IAM roles, networking, and security settings.
Optimize Redshift query performance and storage costs through data distribution styles (ALL, EVEN, KEY), sort keys (SORTKEY), columnar storage, and compression
Integrate Redshift with various AWS services such as S3, Lambda, Glue, Kinesis, EMR, and Business Intelligence (BI) tools like QuickSight and Tableau for end-to
Administer and manage Redshift clusters effectively, including analyzing compression, exporting data with the UNLOAD command, and managing users and permissions
Differentiate between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems and their use cases for transactional and analytical
Requirements
Curiosity and willingness to learn about cloud data warehousing, analytics, and AWS services. No prior Redshift or advanced SQL knowledge is required.
Description
In today's data-driven world, the ability to design and manage scalable cloud data warehouses is a must-have skill. Amazon Redshift is AWS's fully managed, high-performance data warehouse that empowers organizations to analyze data efficiently from terabytes to petabytes.This course is a complete, hands-on guide to mastering Amazon Redshift for real-world data warehousing and analytics. It is designed for data engineers, architects, analysts, BI professionals, and machine learning practitioners who want to build, optimize, and manage powerful analytical solutions in the cloud.You'll start by learning the foundations of OLTP vs OLAP systems and why modern businesses rely on dedicated data warehouses. From there, we dive into Redshift's architecture, including columnar storage, compression, and Massively Parallel Processing (MPP) for high-speed query execution.Through practical, step-by-step lessons, you will:Set up and configure Redshift clusters on AWSConnect using SQL clients and run essential queriesIngest large datasets efficiently with the S3 COPY commandApply data distribution styles (ALL, EVEN, KEY) and sort keys for performanceUse compression and optimization techniques to handle massive workloadsYou'll also explore integrations with key AWS services such as S3, Glue, Kinesis, and Lambda, plus connections with BI tools like QuickSight and Tableau for end-to-end analytics pipelines.By the end of this course, you'll have the skills to design, implement, and optimize scalable Redshift data warehouses making you an in-demand professional in any data-driven organization.
Aspiring data engineers, data architects, and solution architects seeking to design and manage scalable cloud data warehouses on AWS.,Data analysts, business intelligence professionals, and data scientists who need to analyze and report on large datasets efficiently.,Cloud professionals, DevOps engineers, and IT specialists interested in understanding and implementing Redshift within the AWS ecosystem.,Developers and backend engineers aiming to integrate high-performance analytics into applications.,Anyone curious about cloud data warehousing, Big Data analytics, and how Amazon Redshift enables business insights.

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