Operations | Monitoring | ITSM | DevOps | Cloud

Amazon Web Services (AWS) offers a suite of cloud storage services. Among the most widely adopted are Amazon S3 and Amazon EBS, known for their robust scalability, performance, and flexibility for a wide range of workloads. However, AWS also offers Amazon Elastic File System (EFS). This is a serverless file storage service for workloads that need shared, scalable storage for use with AWS services and on-premises resources.

In the microservices world, tracking down performance issues feels like solving a mystery with pieces scattered across dozens of systems. When users report slowness, your team needs answers fast—not hours of guesswork. Distributed tracing is emerged as the solution, but implementing it effectively requires more than just understanding the basics. This guide takes you beyond the fundamentals to show you how DevOps teams and SREs can build truly effective tracing strategies.

You've heard the term thrown around in meetups and Slack channels, but what exactly is full-stack observability? Simply put, you can see, understand, and quickly act on everything happening across your entire tech stack—from frontend user interactions to backend services, cloud infrastructure, and third-party integrations. Full-stack observability isn't just another tech buzzword. It's the difference between being blindsided by outages and catching issues before your users tweet about them.

Slow is officially the new down. That’s a major finding of The SRE Report 2025, with 53% of study respondents agreeing with this expression, and 44% stating “performance should be tracked against a service-level objective.”

According to industry benchmarks, unplanned downtime costs enterprises an average of $5,600 per minute. For industries like fintech, e-commerce, and SaaS, where customer experience is a competitive differentiator, prolonged outages translate into customer churn, SLA penalties, and reputational damage.

Effective logging is essential for any Python application, especially those powering critical backend services. Logs capture diagnostic information about a system’s performance and behavior, enabling better observability and uninterrupted monitoring—both critical as distributed systems grow in complexity. Luckily, Python’s built-in logging module streamlines log management with customizable formats that enhance readability.

Efficient IT Service Management (ITSM) is essential for organizations to manage their IT infrastructure, streamline operations, and improve service delivery. ITSM platforms are the backbone of these efforts by providing tools for incident management, service requests, asset tracking, and more. ITSM platforms help organizations maintain high operational standards while ensuring seamless interaction between the IT teams and other business units.

Today we’re excited to announce that InfluxDB 3 Core, our new open source product licensed under MIT/Apache 2, and InfluxDB 3 Enterprise are now in beta. InfluxDB 3 Core is a high-speed, recent-data engine that collects and processes data in real-time, while persisting it to local disk or object storage. InfluxDB 3 Enterprise is a commercial product that builds on Core’s foundation, adding high availability, read replicas, enhanced security, and data compaction for faster queries.

As Azure environments scale, network complexity multiplies. Expanding subscriptions create visibility challenges that traditional approaches can’t handle. LightMesh simplifies cloud network management, delivering the comprehensive visibility and control teams need to navigate complexity with confidence.