Operations | Monitoring | ITSM | DevOps | Cloud

According to Statista, over 85% of large enterprises and nearly 50% of small to midsize businesses will have adopted microservices as part of their software architecture. The shift is clear: organizations of all sizes are moving away from monolithic applications toward microservices to accelerate development cycles, improve scalability, and support continuous delivery. But this architectural freedom comes with a hidden cost, which increases operational complexity.

We cut log-query scanning from ~100% of data blocks to < 1% by reorganizing how logs are stored in ClickHouse. Instead of relying on bloom-filter skip indexes, they generate a deterministic “resource fingerprint” (hash of cluster + namespace + pod, etc.) for every log source and sort the table by this fingerprint in the primary-key ORDER BY clause. This packs logs from the same pod/service contiguously, letting ClickHouse’s sparse primary-key index skip irrelevant blocks.

The OpenTelemetry Collector is a stand-alone service that acts as a powerful, vendor-neutral pipeline for your telemetry data. It can receive, process, and export logs, metrics, and traces, giving you full control over your observability data before it reaches a backend. This guide will provide a comprehensive overview of the OpenTelemetry Collector, its architecture, deployment patterns, and how to configure it for production use.

Today, I’ll be covering how AWS tags can help you keep track of and monitor your AWS billing costs with the granularity and depth needed to reduce and optimize your AWS costs.

The rising costs and complexities of monitoring cloud infrastructure are pushing many organizations to explore alternatives to Datadog. With monthly bills sometimes reaching thousands of dollars and feature sets that can be overwhelming, teams are looking for practical, cost-effective solutions that better fit their needs.

Modern applications are built using distributed architectures, microservices, and cloud-native technologies. As these systems grow in complexity, it becomes harder for DevOps teams to maintain performance, track issues, and ensure a consistent user experience across all environments. Application Performance Monitoring (APM) helps solve these challenges by providing real-time visibility into how applications behave, from user interactions to backend services and infrastructure.

Application performance management (APM) is the practice of regularly tracking, measuring, and analyzing the performance and availability of software applications. APM helps you get visibility into complex microservices environments, which can overwhelm site reliability engineering (SRE) teams. The generated insights create an optimal user experience and achieve desired business outcomes.

When applications slow down, users leave, and engineering teams scramble. Whether you're troubleshooting a spike in response times or chasing down intermittent backend failures, Application Performance Monitoring (APM) provides the visibility you need to detect, diagnose, and resolve performance issues before they impact your users or business goals. For engineers, APM isn’t just a convenience - it’s essential. But not all APM tools are created equal.