This guide maps the specific metrics, alarm thresholds, and configuration steps for each service, and then addresses the observability delta that CloudWatch leaves unresolved: cross-service correlation, root-cause traceability, and the capacity-planning intelligence that prevents cascades in the first place.

Every industry has its version of the same data engineering problem: massive, complex payloads generated at the edge — far from the cloud, often on unreliable networks — that need to become queryable, structured datasets as fast as possible. In genomics, it is multi-gigabyte sequencing files produced by instruments in labs. 

In autonomous vehicles, it is LiDAR and camera telemetry streaming off test fleets. The underlying architectural challenge is the same in every case: ingest heavy data at burst scale, store it cost-effectively for years, and transform it into something an analyst or ML model can actually use without touching the raw files.

When migrating or running SAP S/4HANA on AWS, many organizations fixate on EC2 instance prices and assume that choosing the cheapest instance types will yield the biggest savings. In reality, cloud TCO is heavily impacted by landscape design choices, how many environments you run, how they’re sized, how data is managed and what auxiliary services you use. Cutting cloud costs isn’t just about shrinking VM sizes it’s about architecting an efficient SAP landscape. As one SAP FinOps guide notes, focusing only on instance sizing addresses symptoms, not causes. True cost optimization asks Is the SAP landscape design efficient? Are you running unnecessary SAP instances, and can workloads consolidate onto fewer systems?. In other words, a thoughtful landscape architecture often yields larger savings than a simple per-server cost reduction.

Understanding an SAP S/4HANA Landscape on AWS

A typical S/4HANA landscape consists of multiple tiers and environments. You might have separate DEV, QA, Staging and Production systems each a full SAP stack with its own HANA database and application servers. On AWS, that could translate to dozens of EC2 instances, along with associated storage and network infrastructure. Each additional environment or system copy multiplies costs for compute, Amazon EBS storage, Amazon EFS shared file systems, backup retention, and so on. Landscape design decisions such as how many parallel systems to run or whether every environment needs high availability can quickly outweigh the cost of an individual EC2 instance.

We've normalized this. It's just become part of the job. But the math is brutal: A team handling 50 incidents per month at 4 to 8 hours of resolve time each is looking at 200 to 400 engineering hours lost. That's a full month of a senior engineer's capacity dedicated entirely to looking backward.

This is what I learned about multi-cloud strategy, not the vendor pitch but the messy reality of keeping production alive across multi-cloud boundaries.

The Problem: "Anonymous" Queries

When your database starts lagging (slow queries), you check the processlist in MySQL only to find a vague line:

Here's the short answer: They're not competing — they're complementary. Each one solves a different problem at a different layer of the agent architecture. Think of them like TCP, HTTP, and HTML — different protocols at different layers that work together to make the web function.

AWS Kiro takes a different bet. Instead of chat-first, it's spec-first. The unit of work isn't a prompt — it's a structured specification that the agent uses to plan, implement, verify, and document your feature end to end. That's a meaningful philosophical difference, and in practice it changes what the tool is useful for.

This is not a hypothetical.

Spring Boot’s Actuator and Micrometer provide rich metrics that can be scraped by Prometheus and visualized in Grafana. This guide covers configuring a Spring Boot application to expose Prometheus-formatted metrics, writing custom metrics, and setting up Prometheus and Grafana for monitoring.

We cover installing Prometheus, writing a configuration to scrape your application, importing Grafana dashboards, and crafting PromQL queries and alerting rules. We also discuss Prometheus best practices, including metric naming conventions, label cardinality, and retention settings. Security considerations, troubleshooting tips, and the performance impact of metrics collection are also included.

I believed this. Genuinely. For a long time.