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--- marc:studie:aws:01_aws_certified_cloud_practitioner [2026/05/14 08:51] – [Pillars Of The Well-Architected Framework] marcv
+++ marc:studie:aws:01_aws_certified_cloud_practitioner [2026/05/14 09:38] (current) – [Pillars Of The Well-Architected Framework] marcv
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 | **Cost Optimization** | Cost optimization involves running systems to deliver business value at the lowest price point possible, while optimizing resource utilization, and maximizing return on investment. |
 | **Sustainability** | The sustainability pillar focuses on continually improving sustainability impacts by reducing energy consumption, thus increasing efficiency across all workload components, and minimizing total resource requirements. |
+Terms used to describe the components and structure of cloud solutions:
+  * **Component:** This refers to the code, configuration, and AWS Resources working together to fulfill a requirement. It is often managed as a distinct unit of technical ownership, independent of other components.
+  * **Workload:** A workload encompasses a set of components collaborating to deliver business value. It is the level of detail typically communicated between business and technology leaders.
+  * **Architecture:** Architecture focuses on how components within a workload interact and communicate. Architecture diagrams illustrate these relationships.
+  * **Milestones:** These are significant points marking changes in your architecture's evolution, from design and implementation to testing, deployment, and production.
+  * **Technology Portfolio:** The technology portfolio comprises the collection of workloads essential for business operations within an organization.
+  * **Level of Effort:** This categorizes the time, effort, and complexity required for task implementation. It is crucial to consider team size, expertise, and workload complexity for context. These levels are as follows:
+    * **High:** Tasks may span **weeks** or **months**, often requiring multiple stories, releases, and tasks.
+    * **Medium:** Tasks are typically completed within **days** or **weeks**, possibly across multiple releases.
+    * **Low:** Tasks are generally finished in **hours** or **days**, possibly involving multiple smaller tasks.
+:!: When architecting workloads, there are several trade-offs between pillars based on business context. These decisions influence engineering priorities, such as optimizing sustainability and cost over reliability in development environments or prioritizing reliability at higher costs for mission-critical solutions. In ecommerce, performance directly impacts revenue and customer engagement. However, **security** and **operational excellence** are pillars that are generally **non-negotiable** and should not be compromised for the sake of others!
+===== General Design Principles =====
+Key design Principles:
+  * **Capacity Planning:** Avoid guesswork by leveraging the flexibility of cloud computing. With on-demand scalability, you can adjust capacity dynamically to match workload demands, while eliminating the need to over-provision or suffer from resource shortages.
+  * **Production-Scale Testing:** Cloud environments enable the creation of realistic test environments on demand. By simulating production conditions, you can conduct thorough testing without incurring the high costs associated with maintaining dedicated testing infrastructure.
+  * **Automation:** Embrace automation to streamline workload management and reduce manual effort. Automated processes facilitate the creation, replication, and monitoring of workloads, allowing for efficient resource utilization and rapid response to changing requirements.
+  * **Evolutionary Architectures:** Traditional architectures often struggle to adapt to evolving business needs due to their static nature. Cloud environments support the evolution of architectures through automation and on-demand testing, while enabling continuous refinement and adaptation to meet evolving demands.
+  * **Data-Driven Design:** Leverage data to inform architectural decisions and optimize workload performance. By collecting and analyzing data on workload behavior, you can identify areas for improvement and make informed decisions to enhance overall efficiency and effectiveness.