Operations | Monitoring | ITSM | DevOps | Cloud

Complete Guide to Redis Monitoring: Essential Metrics, Tools & Best Practices 2025

Redis is a powerful tool, but its position in the critical path of applications means that performance issues can have a widespread impact. Whether you use Redis as a cache, session store, or primary database, effective monitoring is essential to prevent slowdowns and ensure a responsive user experience. This guide provides a comprehensive walkthrough of Redis monitoring, covering the essential metrics you need to track, the tools available to you, and the best practices to adopt in 2025.

Why Observability Isn't Just for SREs (and How Devs Can Get Started)

Almost every other day, when I scroll past r/devops or r/sre, I see a post like this asking how a dev can get started with devops, observability, etc. Sample Reddit thread on how to get started with OTel This blog is an attempt for anyone lost to find their way into observability and a wake-up call for devs to they should think about observability more actively today than ever before. A dev’s observability playbook.

This Month in Datadog: Bits AI SRE, Datadog Data Observability, and more

Datadog is constantly elevating the approach to cloud monitoring and security. This Month in Datadog updates you on our newest product features, announcements, resources, and events. To learn more about Datadog and start a free 14-day trial, visit Cloud Monitoring as a Service | Datadog. This month, we chat with two guests about Bits AI SRE and Datadog Data Observability.

Multi Factor Authentication for Synthetic Monitoring for AVD

Today, I’ll cover some of the basics of monitoring Multi-Factor Authentication and why ensuring MFA is implemented is essential, particularly in environments where remote access is possible. I’ll cover some recent, specific case studies where a lack of MFA has led to security breaches and the mechanisms the bad actors used.

AI Agents Console: Monitor the behavior and interactions of any AI agent in your stack

With Datadog's AI Agents Console, you can monitor the behavior and interactions of any AI agent that’s a part of your enterprise stack, whether that’s a computer use agent like OpenAI’s Operator, IDE agent like Cursor, DevOps agent like Github Copilot, enterprise business agent like Agentforce, or your internally built agents. You'll have full visibility into every agent's actions, insights into the security and performance of your agents, analytics on user engagement, and measurable business value from every agent, all in a centralized location.

New in APM

Datadog’s Latency Investigator for APM—now in Preview—automatically investigates hypotheses in the background, comparing historical traces and correlating change tracking, DBM, and profiling signals. This helps teams quickly isolate root causes and understand impact without combing through raw telemetry data. You can go from detection to resolution in a single workflow, and generate a pull request to apply a recommended fix, all without leaving Datadog..

Data Observability: Build confidence in the data life cycle

Datadog Data Observability provides a complete solution with quality checks (e.g., volume, row changes, freshness), custom SQL-based monitors, anomaly detection, column-level lineage across systems like Snowflake and Tableau, full pipeline visibility, and targeted alerts when data issues arise.

Observing Vercel AI SDK with OpenTelemetry + SigNoz

LLM-powered apps are growing fast, and frameworks like the Vercel AI SDK make it easy to build them. But with AI comes complexity. Latency issues, unpredictable outputs, and opaque failures can impact user experience. That’s why monitoring is essential. By using OpenTelemetry for standard instrumentation and SigNoz for observability, you can track performance, detect errors, and gain insights into your AI app’s behavior with minimal setup.

OpenTelemetry NestJS Implementation Guide: Complete Setup for Production [2025]

NestJS applications require comprehensive monitoring to ensure optimal performance and rapid issue resolution. As your application grows—spanning multiple services, databases, and external APIs—understanding what's happening under the hood becomes critical. That's where OpenTelemetry comes in. OpenTelemetry provides vendor-agnostic observability for your NestJS applications through distributed tracing, metrics, and logs.

Advanced Proactive SSL Certificate Monitoring

eG Enterprise version 7.5 introduces advanced capabilities for detailed SSL Certificate Monitoring including monitoring for web servers and apps using SSL. Monitoring SSL certificates is essential to ensure secure connections, prevent service outages, and maintain user trust. Here are a few things you need to monitor and questions you should ask to keep your services and apps running reliably and securely.

Taming Your Dynatrace Bill: How to Cut Observability Costs, Not Visibility

Dynatrace is a powerhouse for application performance monitoring and business analytics. But for many organizations, its power comes with a significant challenge: as applications scale across complex hybrid environments and diverse tech stacks, the sheer volume and variety of logs, metrics, and traces sent to the platform can explode, leading to staggering and unpredictable costs.

Debug live production issues with the Datadog Cursor extension

The Datadog Cursor Extension uses the Datadog remote MCP Server to give developers access to Datadog tools and observability data directly from within the Cursor IDE. The Cursor Extension enables you to view live variable values that your logpoints capture during execution, and you can use the Cursor Agent to identify the lines of code responsible for the issue at hand. The Datadog Cursor Extension is now available in Preview.

Why Your Business Needs APM: 10 Key Benefits You Shouldn't Ignore

In today’s digital world, how well your applications perform has a big impact on how people see your business, and how well it runs. Whether you are in finance, e-commerce, SaaS, healthcare, or media, your users expect everything to work smoothly, all the time. Even a few seconds of slow performance can lead to lost sales, lower productivity, and unhappy customers. That’s why Application Performance Monitoring (APM) is so important.

Bits AI Dev Agent: Automatically identify issues and generate code fixes

The Bits Dev Agent is an AI-powered coding assistant in Datadog designed to reclaim developer productivity by autonomously monitoring telemetry data, identifying key issues, and generating production-ready pull requests. Developers receive asynchronous, context-rich PRs with clear explanations, allowing them to shift their focus from troubleshooting to reviewing solutions and building better code.

Introducing Bits AI SRE, your AI on-call teammate

Bits AI SRE is your AI on-call teammate, built to autonomously investigate alerts and coordinate incident response. Integrated with Datadog, Slack, GitHub, Confluence, and more, Bits analyzes telemetry, reads documentation, and reviews recent deployments to determine the root cause of alerts—often before you’ve even opened your laptop. In fact, if you're using Datadog On-Call, you can view Bits’s findings right from your phone—so you’re always one step ahead, no matter where you are.

Datadog Incident Response: Unify remediation and communication

With Datadog's new AI voice agent in Incident Response, you can quickly get up to speed on the issue and start taking action directly from your phone. Handoff notifications make it easy to jump straight to the relevant context and quickly communicate with other responders. Finally, our status pages enable you to automatically update users on your remediation progress.

What is Python Application Performance Monitoring? - [A Complete Guide]

A recent study looked at real-world Python programs and found something important: Python isn’t the main reason apps slow down. The real problems come from inside the code like poor logic, memory issues, and slow database queries. The problem is, these issues often go unnoticed. Your app may seem fine until users start complaining about slowness or things start breaking under pressure.

From Sequential Bottlenecks to Concurrent Performance: Optimizing Log Processing at Scale

We optimized log processing pipeline by moving from sequential to concurrent processing at the entry level, achieving 30% higher throughput and better resource utilization without increasing infrastructure costs. When customers start sending millions of logs per minute, you quickly discover whether your processing pipeline can actually scale with vertical scaling.

The Hidden Cost of Not Using APM in Production

Many organizations don’t realize how important it is to monitor how their applications run in production. Without Application Performance Monitoring (APM), it becomes difficult to detect and resolve issues quickly, leading to increased downtime, wasted developer effort, and poor user experience. These hidden costs, though not always visible at first, can impact customer satisfaction, reduce team efficiency, and result in lost revenue.

Golang Application Performance Monitoring: A Comprehensive Guide

Application Performance Monitoring (APM) refers to the practice of tracking, analyzing, and optimizing the performance and availability of software applications. When it comes to Go (Golang), a language known for its concurrency, speed, and efficiency, APM becomes crucial to ensure that your applications stay fast, reliable, and scalable under real-world loads. APM in Go involves monitoring the runtime behavior, request response times, system resource usage, and error patterns across your application.

I built an MCP Server for Observability. This is my Unhyped Take

Recently, I read a blog titled “It’s The End Of Observability As We Know It (And I Feel Fine)”, which discussed MCP servers in observability and how these systems would potentially be the “end of observability”. As someone who has spun up an MCP server for an observability backend and as someone who has been in the space for a while, I certainly do not think so.

Cloud or Self-Hosted - Which Deployment Model is Right For You?

Choosing the right observability platform is a critical decision. But how you deploy it is just as important. The right deployment strategy can accelerate your team, simplify operations, and ensure you meet compliance and security requirements. The wrong one can lead to operational headaches and slow you down. At SigNoz, we believe in flexibility. There is no single "best" way to deploy an observability platform; there's only the way that's best for you.

How APM Can Improve Your Digital Customer Experience?

When a customer taps a button, submits a form or waits for a page to load, they’re not thinking about your backend architecture, microservices, or CDN; they want it to work instantly. But when it doesn’t, the frustration is immediate. Maybe the app freezes. Maybe a checkout fails. Maybe the entire experience just feels laggy. And the worst part? They don't complain, they just leave the application.

Getting started with Dynatrace dashboards

Dynatrace gives you incredibly deep observability data. But all that depth can bury the insights needed. In this blog, we show how to turn Dynatrace's complex telemetry into visual dashboards that actually make sense. Dynatrace is a leading observability and application performance monitoring (APM) platform, known for its deep insight into complex, modern cloud environments. With capabilities spanning infrastructure monitoring, real user monitoring, and security, Dynatrace offers powerful telemetry.

Kubernetes Observability with OpenTelemetry | A Complete Setup Guide

Kubernetes provides a wealth of telemetry data from container metrics and application traces to cluster events and logs. OpenTelemetry offers a vendor-neutral, end-to-end solution for collecting and exporting this telemetry in a standardised format.

Challenges in AIOps and how to sail through them

AIOps (Artificial Intelligence for IT Operations) is not only a game changer, but the need of the hour as modern IT grows and becomes increasingly complex. The promises of AIOps are both overwhelming and tantalizing. AI-powered monitoring and observability can help predict issues, automatically resolve incidents, and optimize performance across the IT infrastructure. However, onboarding an AIOps monitoring tool can be more complicated than it sounds on paper.

Atatus APM: Full-Stack Visibility for Modern Engineering Teams 2025

APM stands for Application Performance Monitoring or Application Performance Management. It helps engineering teams track key metrics, detect slowdowns, and improve the overall performance of their applications. With Atatus APM, you get complete visibility into your application, from backend code and databases to external services and frontend performance.

Datadog vs Jaeger - Features, Pricing & Use Cases [Updated for 2025]

Datadog and Jaeger are both leading tools in the observability space, but they represent two fundamentally different philosophies. Datadog is a commercial, all-in-one SaaS platform that unifies metrics, traces, and logs. Jaeger is a popular, open-source project focused specifically on distributed tracing. Choosing between them isn't just a technical decision; it's about balancing the convenience of a fully managed, integrated platform against the power and control of a self-hosted, specialized tool.

Why APM Is Essential for Microservices Architecture?

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.

Beyond Metrics: How We Reimagined Incident Response with RUM

When your monitoring tools and logs tell you everything's fine, but users can't access critical healthcare services, where do you look? Our team discovered that Real User Monitoring (RUM) isn't just for tracking page load times and user journeys – it's a powerful incident response tool that can uncover issues traditional monitoring misses entirely.

How We Made Our Queries 99.5% Faster

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.

Here's how to add business data to logs from retail endpoints | Datadog Tips & Tricks

Some sources simply do not generate data-rich logs. Retail endpoints that are older or run on proprietary services, for example, very often produce logs without the kinds of data that are needed to perform useful business analytics. So, what can you do?

OpenTelemetry Collector: A Complete Guide [2025]

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.

Comparing The Top 9 Datadog Alternatives and Competitors in 2025

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.

Application Performance Monitoring (APM) Use Cases Every DevOps Team Should Know

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.

APM best practices: Dos and don'ts guide for practitioners

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.

Choosing the Right APM Software: 5 Key Factors to Consider

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.
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Introducing Raygun CLI: Level-up your error tracking workflow

Raygun CLI is a powerful command-line interface tool designed to enhance the developer experience when working with Raygun's error tracking and performance monitoring platform. With this tool, we bring Raygun's features directly to your terminal, making it easier to integrate some important elements of Raygun Crash Reporting and error tracking into your development and CI/CD workflow. We are excited to announce the release of version 1.0.0 of Raygun CLI.

The Complete Guide to APM Best Practices for Developers, DevOps & SREs

Application Performance Monitoring (APM) is no longer optional, it is essential for delivering fast, reliable, and seamless digital experiences. But simply installing an APM tool isn’t enough. To truly know its potential, IT teams need to follow APM best practices. Best practices for APM refer to the most effective ways to monitor, analyze, and optimize your application’s performance using APM tools.

MCP Observability with OpenTelemetry

2025 has truly been the year of Agentic AI, with MCP (Model Context Protocol) emerging as one of its flashy and most talked-about innovations. While many products have seamlessly integrated MCP servers into their systems, these servers are increasingly being labelled as black boxes, opaque components that handle critical tasks but offer little visibility into what's happening under the hood. We prompt an agent, a tool gets invoked, and a response is generated. But what really happens in between?

Perform Distributed Tracing for your MCP system with OpenTelemetry

2025 has truly been the year of Agentic AI, with MCP (Model Context Protocol) emerging as one of its flashy and most talked-about innovations. While many products have seamlessly integrated MCP servers into their systems, these servers are increasingly being labelled as black boxes, opaque components that handle critical tasks but offer little visibility into what’s happening under the hood. We prompt an agent, a tool gets invoked, and a response is generated. But what really happens in between? And when something breaks, how do we trace the failure and debug it effectively?