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Introduction to the five primary types of analytical workloads on AWS: Batch, Interactive, Messaging, Streaming, and Machine Learning. This foundational lesson will define each workload by its characteristics, such as data temperature (hot vs. cold), latency expectations (milliseconds to hours), and common use cases. It will set the context for understanding why different AWS services are designed for specific processing needs.
This lesson covers the essential non-advertising groundwork and technical setup required before launching a Google Ads campaign for lead generation. It focuses on the strategic business prerequisites, such as defining a Unique Selling Proposition (USP) and detailed customer avatars. It then walks through the critical first steps of account creation, emphasizing the importance of selecting "Expert Mode" to gain full control. We will introduce Google Tag Manager (GTM) as the central hub for managing tracking codes and establish the fundamental concepts of conversion tracking, explaining why it is the most critical step to complete before any campaigns go live.
This lesson provides a foundational overview of the core compute services available on Google Cloud. It will start with a deep dive into Compute Engine (IaaS), covering machine types, persistent disks, and specialized options like Sole-Tenant nodes, Preemptible/Spot VMs, and security-focused Shielded/Confidential VMs. The session will then contrast this with serverless platforms, analyzing the use cases for App Engine (Standard vs. Flexible), Cloud Functions for event-driven triggers, and Cloud Run for serving stateless containers. The focus will be on comparing these services to understand the trade-offs between control, flexibility, and operational overhead.
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