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This lesson introduces the fundamental concepts of database models and workloads, which are crucial for selecting the right AWS service. We will first differentiate between relational (SQL) databases, with their structured schema of tables and rows, and non-relational (NoSQL) databases, designed for unstructured data. We will then explore the two primary database workloads: Online Transaction Processing (OLTP) for frequent read/write operations like e-commerce, and Online Analytic Processing (OLAP) for complex queries and data warehousing. Finally, we will introduce Amazon RDS as a managed service that simplifies running relational databases in the cloud.
This lesson introduces the core principles of Site Reliability Engineering (SRE) and the foundational role of observability. We will define reliability as the probability of a system performing its function over time and differentiate it from availability. The main focus will be on the Google Cloud Operations Suite, covering: Cloud Monitoring for collecting time-series data (metrics), Cloud Logging for centralized event log management, and Cloud Alerting for creating proactive notifications based on metric thresholds. We will also briefly touch upon open-source alternatives like Prometheus and Grafana.
This lesson introduces the core concepts of ML model deployment on AWS. It begins by differentiating between training and inference workloads, highlighting the unique requirements of each. We will then survey the primary managed deployment options within Amazon SageMaker, using a decision-making framework to understand when to use each. The focus will be on Batch Transform (for offline bulk inference), Asynchronous Inference (for large payloads and long processing times), Serverless Inference (for intermittent traffic), and Real-Time Inference (for low-latency, persistent endpoints). The lesson will also briefly cover using pre-trained AWS AI Services like Rekognition and Comprehend via their APIs as a starting point for ML integration.
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