In today’s fast-paced software product development world, efficiency and innovation aren’t just important — they’re everything. DevOps engineers and SREs are tasked with maintaining high-performing systems, improving deployment frequency, and ensuring stability while navigating a dizzying amount of tools. Developers who write code also need to learn these tools and are dependent on them to do their jobs. So what’s the problem?

When it comes to monitoring, diagnosing, and optimizing the performance of complex systems today, you can’t really go wrong with tracing tools. And while OpenTelemetry has become the go-to choice for instrumenting apps and collecting traces, there are several other options in the backend that can effectively store, manage, and analyze traces sent by OpenTelemetry. Two of these open-source tools are Jaeger and Grafana Tempo. In this article, we’ll compare and contrast the two.

With the increasing complexity of modern applications, log management solutions have become synonymous with troubleshooting, monitoring, and ensuring application reliability. Moreover, choosing the right tools can significantly impact your application’s performance, efficiency, and overall operational costs. Two powerful tools that often come up in these discussions are Grafana Loki and the ELK Stack (consisting of Elasticsearch, Logstash, and Kibana).

For any digital business today, keeping applications running smoothly and efficiently is a no-brainer. Application Performance Monitoring (APM) is an invaluable process in that regard, helping teams track the performance and health of their software applications. APM involves the use of tools and practices to detect, diagnose, and resolve performance issues in real-time, thereby ensuring optimal user experiences.

ObserveNow, the leading open source-based observability stack, has recently enhanced its capabilities with the introduction of Span Filtering – a key feature in its latest upgrade to Grafana 10.4. This advancement significantly improves the platform’s ability to dissect and analyze traces, which are crucial for understanding the behavior and performance of distributed systems.

There’s no doubt about it: Internal Developer Portals (IDPs) are transforming the way organizations manage their development processes. Gartner defines an IDP as a tool designed to facilitate self-service discovery, automation, and access to various resources within modern software development environments. IDPs are bound to become the backbone of many engineering teams today, and the essential features that engineers seek in an IDP can significantly impact its overall effectiveness.

Implementing a consistent and reliable alerting system across a sprawling organization is a significant challenge for just about any engineering team. For example, diverse infrastructures across different teams and numerous team-specific customizations may not translate well when investigating specific incidents. Inconsistent alerting practices can eventually lead to fatigue, leading to triggering of alerts that may not be relevant or actionable.

Many of our customers frequently ask us how they can calculate the ROI of their observability platforms. It’s a tough question, and one that comes up because company decision-makers often feel like they may be overpaying for observability when things are running smoothly – especially when it comes to their applications.

Every action, transaction, and interaction in an application generates some sort of data. ThisAnd this data holds a wealth of information that, when collected and analyzed over a period of time, provides a comprehensive view of application behavior and performance. Logging is the most widely used technique for collecting data on application states, transactions, errors, and code flow tracking.

Almost everyone acknowledges that log monitoring is essential for maintaining the reliability, security, and performance of modern applications. However, the complexities increase as organizations adopt diverse architectures to effectively manage the various log data challenges they encounter. In our previous blog post, we discussed the significance of log monitoring alongside a few popular log monitoring tools available in the market today.