In the last few years, fintech enterprises have disrupted the financial services and banking industry by taking everything computing technology offers – from machine learning to blockchain – and turning it up a notch. Traditional financial institutions must now compete with challenger banks offering electronic payment alternatives, peer-to-peer lending, and investment apps.

Artificial intelligence for IT Operations (or AIOps) has been playing an expanding role in helping SREs, DevOps, and developers effectively navigate the challenges around application and infrastructure complexity, pace of change, and data volume that characterize the operations landscape.

How is your organization handling Kubernetes observability? What tools are you using to monitor Kubernetes? Is it a time-consuming, manual process to collect, store and visualize your logging, metrics and tracing data? And, what are you actually getting out of all that investment? At Logz.io we’re trying to make this process easier for customers who are serious about Kubernetes observability. We’ve made significant investments in this area for Kubernetes use cases.

If you’ve ever seen Indiana Jones and the Last Crusade, you might remember the scene where Indy and his dad are in a room replete with the most ornate chalices possible, only to realize that the Holy Grail is the most plain, utilitarian one in the room. Windows event logs are the IT version of the plain-looking clay cup that holds the key to answering your service questions and system issues.

Increasingly, the speed and scale of a business can be measured by the resilience and performance of its applications. That’s why organizations are opting to modernize legacy applications by rewriting them using cloud-native tools and platforms. A Gartner study found that by 2025, cloud-native platforms will be the foundation for more than 95% of new digital initiatives, compared to less than 40% in 2021.

This article is the third of a four-part series of articles about Elasticsearch monitoring. In the first article, we put together an Elasticsearch guide, covering how Elasticsearch works and why the setup and tuning of Elasticsearch requires a good knowledge of configuration options and performance metrics.

Let’s say you have a script that works when run in an interactive session, but does not produce expected results when run from cron. What could be the problem? Some potential culprits include: Or it could be something else. How to troubleshoot this then, and where to start? Instead of trying fixes at random, I prefer to start by looking at logs.

Incidents happen. What matters is how they’re handled. Most organizations have a strategy in place that starts with log searches—and logs/log searching are great, but log searching is also incredibly time consuming. Today, the goal is to get safer software out the door faster, and that means issues need to be discovered and resolved in the most efficient way possible.

Observability data provides the insights engineers need to make sense of increasingly complex cloud environments so they can improve the health, performance, and user experience of their systems. These insights can quickly answer business-critical questions like, “what is causing this latency in my front end?” Or, “why is my checkout service returning errors?” Observability is about accessing the right information at the right time to quickly answer these kinds of questions.

Elastic Observability provides a full-stack observability solution, by supporting metrics, traces, and logs for applications and infrastructure. In a previous blog, I showed you how to monitor your AWS infrastructure running a three-tier application. Specifically we reviewed metrics ingest and analysis on Elastic Observability for EC2, VPC, ELB, and RDS.

Does your organization’s data include sensitive information, like intellectual property or personally identifiable information (PII)? Do you want to protect your data from being stolen and sent (i.e., exfiltrated) to external web services? If the answer to these questions is yes, then Elastic’s Data Exfiltration Detection package can help you identify when critical enterprise data is being stolen and exfiltrated.

An effective alerting strategy is the difference between reacting to an outage and stopping it before it starts. That’s why at Coralogix, we’re constantly releasing new features that redefine how alerts are consumed, to enable teams to push their ambitions even further, release with confidence, and tackle issues proactively. Alerts Map is now an indispensable tool for that mission.

Unlock the full potential of your observability stack with continuous profiling Identifying performance bottlenecks and wasteful computations can be a complex and challenging task, particularly in modern cloud-native environments. As the complexity of cloud-native environments increases, so does the need for effective observability solutions.

Cloud Logging’s Log Analytics, with advanced search, as well as aggregation and transformation of all log data types, is now generally available.

As we at Splunk accelerate our cloud journey, we’re often faced with the decision of when to use logs vs metrics — a decision many in IT face. On the surface, one can do a lot by just observing logs and events. In fact, in the early days of Splunk Cloud, this is exactly how we observed everything. As we continue to grow, however, we find ourselves using a combination of both. This post lays out the overall difference in logs and metrics and when to best utilize each.

Prometheus is a widely utilized time-series database for monitoring the health and performance of AWS infrastructure. With its ecosystem of data collection, storage, alerting, and analysis capabilities, among others, the open source tool set offers a complete package of monitoring solutions. Prometheus is ideal for scraping metrics from cloud-native services, storing the data for analysis, and monitoring the data with alerts.

When monitoring your application performance or troubleshooting an issue in production, context is key. The more information available, the faster the prevention of or detection of a user impacting issue. Observability tools offer many different features, like code profiling, to help contextualize your data. In this post, I’ll discuss what code profiling is and show an example of how it works.

The success of your website lies in how satisfied your users are with it. To help ensure the quality of your user experience, Google uses various signals from a web page. The three Core Web Vitals are some of the most important ones. In this article, I’ll talk about what each Core Web Vital means and how to optimize them to deliver a better user experience.

When it comes to centralizing logs to Elasticsearch, the first log shipper that comes to mind is Logstash. People hear about it even if it’s not clear what it does: – Bob: I’m looking to aggregate logs – Alice: you mean… like… Logstash? When you get into it, you realize centralizing logs often implies a bunch of things, and Logstash isn’t the only log shipper that fits the bill.

In an earlier blog post, Log monitoring and unstructured log data, moving beyond tail -f, we talked about collecting and working with unstructured log data. We learned that it’s very easy to add data to the Elastic Stack. So far the only parsing we did was to extract the timestamp from this data, so older data gets backfilled correctly. We also talked about searching this unstructured data toward the end of the blog.

With the growing adoption of remote and distributed application development including micro-services, cloud-native applications, serverless, and more, it is becoming challenging more than ever before for developers to troubleshoot issues within a reasonable time, and that is a bottleneck. That in a sense contradicts the objectives of Agile and DevOps through fast feedback loops, continuous delivery, quick MTTR (mean time to resolution of defects), etc.

We Just celebrated 10 year birthday to Prometheus last month. Prometheus was the second project to join the Cloud Native Computing Foundation after Kubernetes in 2016, and has quickly become the de-facto way to monitor Kubernetes workloads. The plug-and-play experience, just putting Prometheus server and starting to see metrics flowing in tagged with Kubernetes labels, was a compelling offer.

When working with log messages, it’s critical that the timestamp of the log message is accurate. Incorrect timestamps can cause problems when trying to find log messages at a specific date/time or may cause alerts to not function properly. A common cause of incorrect timestamps for log messages is a mismatch of time zones between the log source (device sending the log) and log destination (device receiving the log, such as Graylog).

Grafana Loki is designed to be cost effective and easy to operate for DevOps and SRE teams, but running queries in Loki can be confusing for those who are new to it. Loki is a horizontally scalable, highly available, multi-tenant log aggregation system inspired by Prometheus. It doesn’t index the content of the logs, but rather a set of labels for each log stream.

Monitoring the performance of an application is not a strange concept to most developers. At one point or another, we’ve all had to do some performance debugging of our own. Usually, it happens when there’s a big issue affecting the user’s experience or cost implications. Only then do we make time to look at how the app performs in different scenarios.

Table of Contents When an application written for the Java Virtual Machine is running, it constantly creates new objects and puts them on the heap. Well, at least in the vast majority of the cases. Such objects can have a longer or shorter life, but at some point, they stopped being referenced from the code. Unlike languages like C/C++, we don’t have exact control over when the memory will be freed – freeing the memory is the garbage collector’s job.

Table of Contents Setting up and administering multiple servers for business and application purposes has become easier thanks to advancements in cloud technology. Today, enterprises are choosing to operate large numbers of servers both in the cloud and in their data centers to meet the ever-increasing demand. As a result of these changes, monitoring technologies have become crucial. In this post, we’ll explore the best server monitoring tools and software currently on the market.

In our latest comparison guide for 2023, we'll cover all of the best IT infrastructure monitoring software that you should consider using to maintain uptime and improve your system’s performance.

Curious about Splunk® Universal Forwarders? This article will sum up what they are, why to use them and how the universal forwarder works. Importantly, we’ll point you to the very best tips, tricks and resources on using universal forwarders (and other ways) to get data into Splunk.

In 2017, Just Eat Takeaway.com (JET) was transitioning from a scrappy startup to a surging scaleup. With a global customer base and workforce, the food delivery marketplace’s front line teams needed to scale the real-time monitoring of the platform. Their initial efforts looked like “NASA’s mission control with Grafana dashboards,” said Senior Technology Manager Alex Murray.

Whether you’re a DevOps, SRE, or just a data driven individual, you’re probably addicted to dashboards and metrics. We look at our metrics to see how our system is doing, whether on the infrastructure, the application or the business level. We trust our metrics to show us the status of our system and where it misbehaves. But do our metrics show us what really happened? You’d be surprised how often it’s not the case.

Dashboards are powerful tools for monitoring and troubleshooting your system. Too often, however, we run into an incident, jump to the dashboard, just to find ourselves drowning in endless data and unable to find what we need. This could be caused not just by the data overload, but also due to seeing too many or too few colors, inconsistent conventions or the lack of visual cues.