Mon.Jun 09, 2025

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Unified observability: Why storing OpenTelemetry signals in one place matters

Dynatrace

In observability’s early days, we often talked about the “three pillars.” That is, traces, logs, and metrics, which gave us the information to make our systems observable. The problem with referring to these three signals as “pillars” is that it implies they’re siloed and therefore independent of each other, when in fact, the exact opposite is true.

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From Code to Customer: Building Fault-Tolerant Microservices With Observability in Mind

DZone

Microservices have become the go-to approach for building systems that need to scale efficiently and stay resilient under pressure. However, a microservices architecture comes with many potential points of failure—dozens or even hundreds of distributed components communicating over a network. To ensure your code makes it all the way to the customer without hiccups, you need to design for failure from the start.

Code 100
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How to Perform a Disaster Recovery Switchover with Patroni for PostgreSQL

Percona

Patroni is a Python-based template for managing high availability PostgreSQL clusters. Originally a fork of the Governor project by Compose, Patroni has evolved significantly with many new features and active community development. It supports integration with various Distributed Configuration Stores (DCS) like etcd, Consul, and ZooKeeper, and provides simple setup and robust failover management.

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How You Can Use Few-Shot Learning In LLM Prompting To Improve Its Performance

DZone

You must’ve noticed that large language models can sometimes generate information that seems plausible but isn't factually accurate. Providing more explicit instructions and context is one of the key ways to reduce such LLM hallucinations. That said, have you ever struggled to get an AI model to understand precisely what you want to achieve? Perhaps you've provided detailed instructions only to receive outputs that fall short of the mark?