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--- marc:studie:aws:01_aws_certified_cloud_practitioner [2026/05/14 09:00] – [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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 :!: 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.