When Logz.io was founded in 2015, we set out to simplify logging with the ELK Stack by delivering Elasticsearch and Kibana as a managed cloud service. But logs only tell part of the story – DevOps teams also need metric and trace data to better monitor the health and performance of their environment and quickly pinpoint the root cause of new problems. Importantly, using multiple tools to collect and analyze this data adds complexity and extra work.

At observIQ, we pride ourselves on delivering simple and powerful functionalities, quickly. We’re excited to announce the addition of Live Tail to the observIQ featureset. Live Tail emulates the terminal experience, giving you the ability to analyze, visualize and debug live – all in a single place. Never be worried about what the outcome of your deployment will be because Live Tail lets you troubleshoot, react and reassess issues in your deployment in real-time.

Azure, AWS, and GCP cloud services are invaluable to their enterprise customers. When providers like Microsoft are hit with DNS issues or other errors that lead to downtime, it has huge ramifications for their users. The recent Azure cloud services outage was a good example of that. In this post, we’ll look at that outage and examine what it can teach us about enterprise cloud services and how we can reduce risk for our own applications.

When you grow your peak concurrent users by 5x nearly overnight, ensuring that your operations can successfully support that growth can be a make or break for your success. Rocket League is a popular online multiplayer game created by Psyonix described as arcade-style soccer and vehicular mayhem. In the summer of 2020, the game maker decided to switch the business model of the game from an upfront purchase to a free to play model.

Kubernetes is an open source container orchestration system for automating computer application deployment, scaling, and management, and seems to have established itself as the de facto standard in this area these days. The shift from monolithic applications to microservices brought by Kubernetes has enabled faster deployment, where dynamic environments become commonplace. But on the other hand, this has made monitoring applications and their underpinning infrastructure more complex.

You may have seen the announcement that Splunk and McLaren Racing have expanded their partnership, which sees Splunk as an Official Global Partner of the McLaren Shadow Esports team and the Logitech McLaren G Challenge. As a budding Esports fan and data enthusiast, it’s really exciting to see these two worlds collaborate and accelerate the virtual racing experience.

We all know that faster is better. Research and results clearly indicate that faster experiences with fewer errors result in increased usage, conversion, and revenue. With the desire to improve business metrics in mind, organizations often seek immediate improvements in customer experience across digital properties. However, without proper planning and coordination, these attempts consistently fail.

Game development is an entirely different beast to other industries. Marketing, development, and release are more tightly interwoven than in other sectors, with a lot of pressure to meet community-anticipated milestones and launch. As such, it’s important to have game engine logging and monitoring pipelines set up for your projects. In other platforms, version upgrades and roll-outs tend to be sudden, with no definitive date set.

“If you ain't first, you're last.” While that famous one-liner from Ricky Bobby (Will Ferrell) in the cult hit Talladega Nights is more joke than catchphrase, it hits home for those of us in the world of DevOps and Observability. Faster is better. And in our technology-driven world of online transactions and complex environments, faster isn’t just better — it’s crucial.

Modern IT environments have presented many difficult-to-overcome challenges to organizations in recent times. One such challenge is gaining visibility into the systems. One may argue that due to cloud computing and limitless storage, it is now very easy to overcome some of the conventional challenges regarding visibility. However, the architecture has changed into infrastructure scheduling and microservices. Hardware and software programs are now more complex, with their own set of challenges.

The team at observIQ is just like every one of you reading this, we are avid programmers, gamers, traders, thinkers, and innovators who build an elaborate home network for fun, work, and for the simple reason that we enjoy technology. We are constantly growing the size and footprint of our home networks and labs as well – adding custom apps, devices, and servers, making it challenging to gauge our technical footprint.

When you’re operating a web application, the last thing you want to hear is “the site is down." Regardless of the reason, the fact that it is down is enough to cause anyone responsible for an app to break out into a sweat. As soon as you become aware of an issue, a clock starts ticking — literally, in some cases — to get the issue fixed. Minimizing this time between an issue occurring and its resolution is arguably the number one goal for any operations team.

Operational resilience is currently a hot topic in Financial Services, largely because of the impact that COVID has had on how customers interact with financial institutions. Almost overnight, the financial services industry had to cope with a large volume of transactions moving to digital channels at the same time as its employees were forced to set up home offices so that they could continue to work remotely.

In this post, we will discuss some key considerations and strategies to collect and analyze your AWS Lambda logs. This will include 1) what to know about logging Lambda functions, 2) how to ship log data to a centralized logging solution, and 3) how to search and visualize log data on monitoring dashboards.

As organizations migrate to Elastic from incumbent vendors, quickly onboarding log data from their current solution into Elastic is one of the first orders of business. Data onboarding often involves having to adjust ingestion architecture and implement configuration changes across data sources. We want to ensure that users trialing or migrating to Elastic can get data in quickly to start seeing the power of Elastic solutions as quickly as possible.

Our Kibana team has been hard at work implementing and executing on a new Kibana strategic vision to streamline the dashboard creation process and sand down the rough edges of creating visualizations for dashboards. We accomplished our goal and reduced the overall time it takes users to go from a blank slate to a meaningful dashboard that conveys insights about the data.

For DevOps teams that want to accelerate release velocity and improve reliability, logs can unlock the insights you need to move faster. But for managers and budget owners, logging can be an unpredictable pain. Trying to estimate logging spend, especially with the adoption of microservices and container-based architecture, seems like an impossible task.

The LogDNA Agent is a powerful way for developers and SREs to aggregate logs from their many applications and services into an easy-to-use web interface. With only 3 kubectl commands, the installation process is quick and simple to complete for any number of connected systems. To help control the logs that are stored and surfaced in the LogDNA web interface, users can set Exclusion Rules, which enables the exclusion of certain queries, hosts, and tags directly from the UI.

The aim of this article is to demonstrate how you can instrument a Java application using Opentelementry and Jaeger. In this example, we will be instrumenting our Java application using OpenTelemetry and the OpenTelemetry Java client, and the tracing data will be exported and visualized using Jaeger. We will use the Logz.io Jaeger backend as it is compatible with common tracing standards like Zipkin, OpenTelemetry, and OpenTracing.

As my colleague, Tim Frank, wrote about recently in his blog post, "The Department of Defense Data Strategy: An Important Start," in late 2020 the Department of Defense (DoD) released its new Data Strategy — providing focus and direction for the Department’s efforts to become data-centric at all levels of its enterprise.

Previously, I wrote a Beginner’s Guide to Jaeger + OpenTracing Instrumentation for Go providing guidance on manually instrumenting Go services. This is useful for cases where we want fine-grained tracing of specific functions. However, what if all we want is to trace a service’s inbound and outbound calls with little to no additional code?

When building a microservices system, configuring events to trigger additional logic using an event stream is highly valuable. One common use case is receiving notifications when errors are seen in one of your APIs. Ideally, when errors occur at a specific rate or frequency, you want your system to detect that and send your DevOps team a notification. Since AWS APIs often use stateless functions like Lambdas, you need to include a tracking mechanism to send these notifications manually.

Visibility into your Salesforce environment is crucial for keeping your data secure and ensuring a seamless user eperience. That’s why we are excited to announce that Datadog can now collect Salesforce event logs directly from your Real-Time Event Monitoring stream, giving you deep insights into the security and operational performance of your Salesforce environment.

Elastic Cloud on Kubernetes (ECK) is an easy way to get the Elastic Stack up and running on top of Kubernetes. That’s because ECK automates the deployment, provisioning, management, and setup of Elasticsearch, Kibana, Beats, and more. As logging and metric data — or time series data — has a predictable lifespan, you can use hot, warm, and cold architecture to easily manage your data over time as it ages and becomes less relevant.

On Tuesday June 8th, the Content Delivery Network Fastly experienced an outage that made large swaths of the web unavailable for nearly an hour. To focus on the positive, this outage can serve as a wakeup call for Observability teams, because it shows how much modern sites depend on resources beyond their immediate control, and how hard it is to "observe" these kinds of issues with an incomplete Observability mindset.

Monitoring is crucial if you want to see what happens in your system and JVM-based applications are not different. Well, some metrics, like memory and garbage collection, require special attention because they play a major role in your application performance. In this blog post, we will look into the key Java Virtual Machine (JVM) metrics that you should monitor if you care about performance and stability. Those are the memory, the garbage collection, and the JVM threads.

Customers need scale and flexibility from their cloud and this extends into supporting services such as monitoring and logging. Google Cloud’s Monitoring and Logging observability services are built on the same platforms used by all of Google that handle over 16 million metrics queries per second, 2.5 exabytes of logs per month, and over 14 quadrillion metric points on disk, as of 2020.

Ivanti Device Control is all about securing your endpoints while also providing a detailed overview to quickly identify weak links in your environment. The latter has now become much simpler and quicker to perform! Our new Splunk connector enables you to connect directly to your Ivanti Device Control environment, feeding in all reported events and showing you the most important data in a single dashboard.

Today’s business is powered by data. Success in the digital world depends on how quickly data can be collected, analyzed and acted upon. The faster the speed of data-driven insights, the more agile and responsive a business can become. Apache Kafka has emerged as a popular open-source stream-processing solution for collecting, storing, processing and analyzing data at scale.

In a previous blog post, "Monitoring Kafka Performance with Splunk," we discussed key performance metrics to monitor different components in Kafka. This blog is focused on how to collect and monitor Kafka performance metrics with Splunk Infrastructure Monitoring using OpenTelemetry, a vendor-neutral and open framework to export telemetry data. In this step-by-step getting-started blog, we will.

Today, we’re excited to announce a new completely free pricing tier for observIQ: the 3-day free plan. With the observIQ free plan, you can ingest and index up to 3 gigabytes of logs per day with a 3 day rolling retention period.

Being able to get the big picture and immediately pivot between siloed data is one of the key values Grafana Cloud provides. Our composable observability platform integrates Prometheus and Graphite metrics, Loki logs, and Tempo traces with Grafana — and also allows you to draw data in from other sources of your choice concurrently.

In January 2021, we announced that starting with version 7.11, we would be changing the Apache 2.0 portions of Elasticsearch and Kibana source code to be dual licensed under Elastic License and SSPL, at the users’ discretion. As part of that change, we created Elastic License 2.0 (ELv2) as a permissive, fair-code license, which allows free use, redistribution, modification, and derivative works, with only three simple limitations, outlined in our original announcement.

Salesforce was the first of many SaaS-based companies to succeed and see massive growth. Since they first started out in 1999, Software-as-a-Service (SaaS) tools have taken the IT sector and, well the world, by storm. For one, they mitigate bloatware by moving applications from the client’s computer to the cloud. Plus, the sheer ease of use brought by cloud-based, plug-and-play software solutions has transformed all sorts of sectors.