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This course is the second of two offerings designed to introduce learners to the current market trends in analytics. Building upon the concepts introduced in Part 1, this course introduces learners to an overview of data lakes, data warehouses, and modern data architectures on AWS. You will learn about which AWS services can be used to build a data warehouse, data lakes, and modern data architectures on AWS. You will also see common modern data architecture use cases and a reference architecture. This course includes: lessons, videos, scenarios, and knowledge check questions.

Amazon Q Business is generative artificial intelligence (generative AI) powered assistant that can answer questions, generate content, create summaries, and complete tasks—all based on the information in your enterprise. In this Getting Started course, you will learn about the benefits, features, typical use cases, technical concepts, and cost of using Amazon Q Business. You will also review an architecture that depicts how Amazon Q Business works. Through a guided tutorial consisting of a narrated video, step-by-step instructions, and transcript, you will learn how to create an Amazon Q web experience that uses a sample set of documents.

Foundational overview of AWS Trainium and Inferentia, the purpose-built silicon for training and inference. Covers chip architectures, the Neuron SDK, and when to choose Trainium- or Inferentia-based EC2 instances over GPU alternatives.

Introduction to AWS AI Factories, fully managed AWS AI infrastructure deployed inside customer data centers. Covers reference architectures, integration with Amazon Bedrock and SageMaker, and use cases for sovereign and on-premises AI workloads.

Foundational deep dive into Amazon Aurora, covering its distributed storage architecture, replication, failover behavior, performance characteristics, and the differences between Aurora Standard, I/O-Optimized, and Serverless v2 configurations.

Architectural overview of PostgreSQL internals, including process and memory architecture, the write-ahead log, MVCC, the buffer manager, and how these mechanics behave when running on Amazon RDS and Aurora PostgreSQL.

Overview of Amazon AppStream 2.0, AWS's fully managed application streaming service. Covers core architecture, image and fleet provisioning, user access methods, and common deployment scenarios for streaming desktop applications to any HTML5-capable browser.

Introduction to Amazon Aurora, AWS's MySQL- and PostgreSQL-compatible relational database engine. Covers cluster architecture, storage and replication design, performance characteristics, and how to provision and connect to an Aurora cluster.

Security essentials for media and entertainment content production on AWS, including MovieLabs-aligned reference architectures, identity controls, encryption, and protecting pre-release assets across cloud production workflows.

Patterns and AWS services for designing event-driven architectures, including Amazon EventBridge, SNS, SQS, Kinesis, and Step Functions. Covers event schemas, choreography versus orchestration, and resilience considerations.

Enterprise architecture patterns for Amazon Elastic Container Service, including multi-account designs, service discovery, mesh integration, observability, and choosing between EC2 and Fargate launch types at scale.

Introduction to AWS Inferentia and Amazon EC2 Inf1 instances for cost-effective machine learning inference. Covers chip architecture, the Neuron SDK, and migration patterns from GPU-based inference deployments.

Introduction to Amazon EC2 P6e-GB300 UltraServers, accelerated by NVIDIA GB300 NVL72. Covers the UltraServer architecture, near-20TB GPU memory footprint, and use cases for trillion-parameter model training and inference.

Comparison of Amazon Aurora MySQL and Amazon RDS for MySQL, focusing on differences in architecture, performance, high availability, and pricing. Helps practitioners choose between the two managed MySQL options based on workload requirements.

Conceptual primer on Amazon Relational Database Service, including supported engines, the shared responsibility model for managed databases, instance and storage architecture, and core operational features such as backups, snapshots, and Multi-AZ.

Introductory module for the advanced PostgreSQL learning plan covering Amazon Aurora and RDS. Outlines the curriculum across architecture, performance, security, monitoring, and migration topics.

Foundational walkthrough of the AWS Well-Architected Framework and its six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. Introduces the Well-Architected Tool and review process.

Foundational course on applying generative AI to SAP workloads running on AWS. Covers integration patterns with Amazon Bedrock, common SAP use cases such as document processing and analytics, and reference architectures.

Guidance on applying the AWS Well-Architected Framework to agentic AI applications. Covers operational, security, reliability, performance, cost, and sustainability considerations specific to autonomous agent systems.

Amazon Q in QuickSight introduces a new suite of business intelligence (BI) capabilities by using the large language models (LLMs) of Amazon Bedrock and combining them with the capabilities of Amazon QuickSight. In this course, you will learn about technical concepts and the benefits of using Amazon Q in QuickSight. You will learn about the architecture of Amazon Q in QuickSight and how the built-in features help you to build dashboards and derive insights from your data with natural language queries. Course level: Fundamental. Duration: 60 minutes. This course includes presentations, demonstrations, and assessments.

Foundational course covering core AWS Cloud concepts, services, security, architecture, pricing, and support. Designed for those new to the cloud and aligned to the AWS Certified Cloud Practitioner (CLF-C02) exam.
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