We built a monitoring system for a cryptocurrency backend that provides deep observability while protecting sensitive financial data through layered health checks and resilient, security-first architecture.

Monitoring the ICY Swap backend

A technical case study for implementing centralized monitoring for a trading platform using Grafana and Prometheus, focusing on real-time alerts, data integrity, and resource optimization to prevent financial losses.

Setup centralized monitoring system for Hedge Foundation trading platform

Learn about our dual approach to monitoring at memo.d.foundation, combining synthetic and instrumental monitoring to ensure system health and community engagement.

Monitoring

A guide to building effective monitoring systems, covering components, implementation, metrics, correlation, and scaling strategies for tech teams.

§ Monitoring

How we build our monitoring system

A practical guide to building monitoring systems that actually work. We share our approach to making smart decisions about what to monitor, how to collect data, and when to add complexity.

Approach to building monitoring

Hard-learned lessons from building monitoring systems in production. We share common mistakes to avoid and practical strategies for growing your monitoring capabilities over time.

Monitoring best practices

Understanding the building blocks of effective system monitoring. We break down synthetic monitoring, instrumentation, logging, and tracing to help you build observability into your applications.

Monitoring components

How to connect metrics, logs, and traces for faster debugging. We explain the investigation flow and practical strategies for correlating data across your monitoring stack.

Correlation layer

Practical implementation guidance for setting up comprehensive monitoring across all aspects of your system - from infrastructure to code quality and team performance.

Implementation guide for monitoring systems

A comprehensive prompt template to get tailored metrics recommendations for any system. Use this with ChatGPT, Claude, or other LLMs to design monitoring that actually matters.

LLM prompt for metrics planning

A practical guide to choosing the right metrics for system health monitoring. We cut through the noise to focus on metrics that directly impact your users and business.

Which metrics actually matter

Understanding when to use trace_id versus request_id, and how to implement correlation that actually helps during incidents. We explore practical patterns for different system scales.

Trace IDs and request IDs that work

Understanding when to invest in comprehensive monitoring versus keeping it simple. We explore how system scale, user impact, and team resources should drive your monitoring decisions.

Does monitoring scale with your system?

Exploring when synthetic monitoring alone makes sense and how to maximize its value when you can't instrument your systems or need to keep teams separate.

Can you rely mostly on synthetic monitoring?

Discover how Dwarves Foundation uses Upptime and GitHub Actions for transparent public uptime monitoring while securely keeping tabs on internal services.

#monitoring

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