Operations | Monitoring | ITSM | DevOps | Cloud

The world is changing every day. From geopolitical shifts to legislation like GDPR which requires localized processing – these all create a complex and uncertain landscape where data storage, processing, and cloud services could potentially come to a sudden halt or suffer heavy disruption overnight. As a result, organizations are increasingly interested in potential routes for shifting cloud services to safer alternatives closer to their country of operation.

Both Grafana Tempo and Jaeger are distributed tracing tools designed for modern microservice architectures. Jaeger, released as an open-source project by Uber in 2015, has matured into a graduated CNCF project. Tempo, announced by Grafana Labs in October 2020, is a newer entrant focused on high-volume tracing with a unique storage architecture. Before comparing these tools in detail, let's quickly review what distributed tracing is and why it matters.

When something breaks in production, logs are usually where you start. They help you figure out what happened, where, and why. But with microservices architecture, logging isn't simple anymore. In a traditional monolithic application, logs live in one place. With microservices, they're scattered across multiple services, containers, and sometimes even data centers. What used to be a simple grep command now feels like solving a mystery without most of the clues.

It came as a pleasant surprise. CloudZero recently discovered over $1.7 million in annualized savings. And, all from our own infrastructure. Otherwise, we would have spent $1.7 million on cloud resources we didn’t actually need. Cloud savings stories like this are common with CloudZero customers, and we often find companies spending more than they should in the cloud. If your team is also struggling with managing and controlling the cost of cloud computing, then this guide is for you!

IT Ops Problem Solver Series – Part 2: This article is a summary of a full report in our IT Ops Problem Solver Series. In this series, we’ll tackle the biggest problems facing IT Ops leaders and explore how some of Galileo’s clients are addressing them. In this part of the series, we delve into IT performance challenges and how to address them effectively.

Apache Flink is an open-source, distributed stream processing engine built for real-time, high-throughput data pipelines. It excels at processing continuous data streams with low latency, making it a great fit for use cases like fraud detection, log analytics, real-time dashboards, personalized recommendations, and IoT telemetry.

Monitoring deployed applications with eBPF is quickly becoming the standard for good reasons, eBPF: Revolutionizing Observability for DevOps and SRE Teams. Not in the least because it allows monitoring to be a purely operations affair, instead of having to instrument each and every application individually. The security-conscious SRE and SRE manager will immediately ask the question: is this secure? And how about this claim that HTTPS traffic can be monitored?

If you’re a technology reseller in today’s uncertain software virtualization market, you and your customers are probably actively exploring options for virtualized environments. Of course, finding the perfect alternative is easier said than done: it needs to be scalable, easy to migrate to, cost efficient, and provide the same technical capabilities as preexisting virtualization solutions. That’s a seemingly tall order – but luckily, there are a range of options.

With advances in AI and heightened compliance requirements, the way we handle data is changing rapidly. This makes a robust database change management process crucial for organizations who still need to ship value to market, while getting the right data into the hands of decision-makers faster. It’s a tough set of demands for any organization to grapple with as they modernize their data for the future.