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This lesson introduces the core principles of designing resilient systems on AWS. It begins by defining resiliency and availability, using the industry-standard "nines" (e.g., 99.9%, 99.99%) to quantify uptime and acceptable downtime. The main focus will be on two key AWS services: EC2 Auto Scaling and Simple Queue Service (SQS). We will cover how to configure an Auto Scaling group using Launch Templates, including setting minimum, maximum, and desired capacity. We will also explore dynamic scaling policies, specifically Target Tracking, to automatically adjust capacity based on metrics like CPU utilization. The second part of the lesson will explain how to use SQS to decouple application components, improving fault tolerance. This includes the producer/consumer pattern, the function of a Dead-Letter Queue (DLQ) for handling message failures, and the benefits of loose coupling in preventing cascading failures.
Introduction to Retrieval-Augmented Generation (RAG). This lesson will define what RAG is, contrasting it with other LLM customization techniques like fine-tuning. We will cover the core architectural components: the Retriever, the Augmenter, and the Generator. The focus will be on the 'why' – understanding the specific problems RAG solves, such as reducing hallucinations and enabling knowledge-intensive tasks with up-to-date or proprietary information. We will discuss real-world business use cases, like building a Q&A chatbot over internal company documents or a customer support bot using a knowledge base.
This lesson provides a foundational overview of the history and core concepts of data analytics, leading into the modern AWS data analytics pipeline and the concept of a data lake, based on the provided document 'History of Analytics and Big Data'. The session will start by covering the historical evolution from traditional data warehousing to the current 'New World Order' driven by technologies like Hadoop and cloud computing. We will then dissect the modern analytics pipeline, covering the key stages: Collection, Storage (hot, warm, cold data), Processing, and Visualization (referencing the Gartner Analytics Maturity Model). The majority of the lesson will focus on mapping these conceptual stages to the AWS Big Data Reference Architecture, identifying core services like Amazon S3, AWS Glue, Amazon Kinesis, and Amazon QuickSight. Finally, the lesson will define the data lake concept and briefly outline the five steps to building one on AWS, positioning AWS Lake Formation as a service that simplifies this process. The lesson will conclude with a recommendation to attempt the assessment questions from the chapter to self-evaluate understanding.
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