Transform your data analytics capabilities with our Amazon Redshift course, tailored for data engineers and architects to master cloud-based data warehousing for optimized analytics solutions.
EnrollDesign and implement comprehensive data warehouse solutions using Amazon Redshift.
Optimize data storage and processing to enhance analytics performance.
Apply security measures and cost management strategies effectively.
Integrate and manage data pipelines for advanced analytics and machine learning.
In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.
Data analytics use cases
Using the data pipeline for analytics
Why Amazon Redshift for data warehousing?
Overview of Amazon Redshift
Amazon Redshift architecture
Interactive Demo 1: Touring the Amazon Redshift console
Amazon Redshift features
Practice Lab 1: Load and query data in an Amazon Redshift cluster
Ingestion
Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
Data distribution and storage
Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
Querying data in Amazon Redshift
Practice Lab 2: Data analytics using Amazon Redshift Spectrum
Data transformation
Advanced querying
Practice Lab 3: Data transformation and querying in Amazon Redshift
Resource management
Interactive Demo 4: Applying mixed workload management on Amazon Redshift
Automation and optimization
Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster
Securing the Amazon Redshift cluster
Monitoring and troubleshooting Amazon Redshift clusters
Data warehouse use case review
Activity: Designing a data warehouse analytics workflow
Modern data architectures
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Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier
Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier