SolarWinds Inc., based in the United States, is a software company that helps businesses manage their networks, systems, and IT infrastructure. Its headquarters are in Austin, Texas, and it has sales and product development offices around the United States and in a number of other countries. It has acquired a number of other businesses, including Pingdom, Papertrail, and Loggly, which it continues to operate under their original names.

Logz.io is a SaaS (software as a service) provider with an observability offering made up of various managed open source technologies. These technologies include the Elastic Stack for logging and SIEM (security information and event management), Prometheus, for monitoring, and Jaeger for tracing. The company positions itself as an alternative to the Elastic Stack (or ELK Stack), which is made up of Elasticsearch, Logstash, Kibana, and Beats.

I recently wrote about the importance of logging and how developers often overlook it in the software development process. Now that you’re convinced that you should be logging throughout the SDLC, it’s time to choose a reliable and dedicated log management platform.

IT professionals love their metaphors. From “pets vs. cattle” to “post mortems” to “fog computing” and beyond, practitioners tend to use analogies to shape the way they think about complex technical topics. Here’s another analogy: Log looms.

Michele Mancioppi is a product manager at Canonical with responsibility for observability and Java. He is the architect of the new system of Charmed Operators for observability known as LMA2. Jon Seager is an engineering director at Canonical with responsibility for Juju, the Charmed Operator Framework, and a number of Charmed Operator development teams which operate across different software flavors including observability, data platform, MLOps, identity, and more.

We’ve had a busy week in the Splunk Partnerverse! In addition to the new Splunk Partnerverse Program announced last week, we also shared some significant partnership news with one of our longest-standing partners, Accenture.

When teams begin to analyze their logs, they almost immediately run into a problem and they’ll need some JSON logging tips to overcome them. Logs are naturally unstructured. This means that if you want to visualize or analyze your logs, you are forced to deal with many potential variations. You can eliminate this problem by logging out invalid JSON and setting the foundation for log-driven observability across your applications.

We’re all familiar with the typical use cases for log management, such as monitoring cloud infrastructures, development environments, and local IT infrastructure. So we thought it would be fun to cover some of the less usual, more wild use cases for log management, just to show that log management tools are more versatile, and more interesting, than they may seem. If any of these use cases look too interesting to ignore, let us know and we can do a full article on them!

Engineering teams hoping to gain full-stack observability into their environment need access to the relevant logs, metrics, and traces generated by their cloud infrastructure and applications. Accessing the relevant data quickly is essential – not just because it is more convenient, but because faster engineers are also business-critical for many organizations.

OpenTelemetry is the recommended path today for instrumenting applications with tracing in a standard, vendor-agnostic and future-proof way. In fact, OpenTelemetry (nicknamed OTEL) encompasses all three pillars of observability: tracing, metrics, and logs. The tracing element of the specification is now stable with the rest following. This is innovative stuff! You can read more on OpenTelemetry and the current release state on this guide.

Not every organization is - or even wants to be - a Fortune 500. Unfortunately, cybercriminals don’t care how big your company is. In fact, they often look to target small and midsize businesses (SMBs) knowing that they might have fewer security resources. You have the same problems that the big companies have, but you also have less money and people. Using centralized log management can give you the security solution you need, at a price you can afford.

Image via Pixabay As any developer or IT professional will tell you, when systems experience issues, logs are often invaluable. When implemented and leveraged effectively, the data produced by logging can assist DevOps teams in more quickly identifying occurrences of problems within a system. Moreover, they can prove helpful in enabling incident responders to isolate the root cause of the problem efficiently. With that being the case, maximizing the value of log data is vital.

Log management software gives the primary diagnostic data in your applications’ development, deployment, and maintenance. However, choosing the log sources to log and monitor could often be a daunting task. The primary cause of concern in monitoring all sources is the high price tag that many SIEM tools in the market charge based on the number of users and sources ingesting logs. At observIQ, we offer unlimited users and sources.

We just announced the creation of a new RemoteWrite SDK to support custom metrics from applications using several different languages. This tutorial will give a quick rundown of how to use the Java SDK. This SDK – like the others – is completely free and open source, and is meant to apply to any output destination, not just Logz.io.

We’re excited to announce the general availability of the LogDNA Agent 3.3, which introduces Linux and ARM64 support to our Rust Agent. This new support in our Rust Agent provides improved performance and enables a few features previously only available for our Kubernetes customers, such as various configurations within the Agent and the ability to run as a non-root user. Additionally, we have added in Prometheus Metrics that help provide insights into your Agent.

Log observability and monitoring are terms often used interchangeably, but really they describe two approaches to solving and understanding different things. Observability refers to the ability to understand the state of a complex system (or series of systems) without needing to make any changes or deploy new code.

Visualizing log data is one of the biggest perks of using good log management software. Data is many businesses’ most critical asset. But, without proper use, a business’ data becomes just an artifact and no longer an asset. Visualization and analysis are the end goals of collating log data from their sources. The need for visualization arises from the fact that we intuitively process visual information faster than a random jumble of numbers and letters.

In today’s highly interconnected worlds, CISOs face a dual challenge: protecting data and reporting to the Board of Directors. Log management has long been a tool in the CISO’s back pocket, helping gain insight into potential security issues. However, the rise of cloud-based infrastructures changes this, making log management increasingly difficult.

Consider the following question: Why do most teams face pressure to rethink traditional logging and observability approaches? Asking this question to most engineers would likely result in answers centered on the challenges posed by microservices apps. Because microservices are more complex than monoliths and involve more moving parts, they require more sophisticated, granular log collection, correlation, and analysis.

We all know the importance of cataloging, organizing, and breaking down the data in your logs. That process, parsing, makes information easier to find and simplifies subsequent analysis. Now, with Logz.io’s upgraded self-parsing tool, custom parsing rules, and log organization is easier than ever. What’s important is parsing that data out correctly. The better parsed, the easier to query.

Tucker Callaway is the CEO of LogDNA. He has more than 20 years of experience in enterprise software with an emphasis on developer and DevOps tools. Tucker drives innovation, experimentation, and a culture of collaboration at LogDNA, three ingredients that are essential for the type of growth that we've experienced over the last few years.

Which data sources do DevOps teams need in order to achieve observability? At a high level, that’s an easy question to answer. Concepts like the “three pillars of observability”—logs, metrics, and traces—may come to mind. Or, you may think in terms of techniques like the RED Method or Google’s Golden Signals, which are other popular frameworks for defining which types of data teams should collect for monitoring and observability purposes.

Many companies in Asia Pacific (APAC) were caught in a digital tailspin when Covid-19 hit, sacrificing security practices in their hurry to adjust to the new reality of remote work. Two years on, hybrid work is still the norm as the pandemic continues and seems to be a new way of life moving forward. Catalyzed by the coronavirus, firms big and small are now adopting cloud technologies as we tread deeper into a new data age.

We are happy to provide an open-source Terraform module enabling automatic delivery of CloudTrail logs from S3 to a Kinesis Data Stream.

A Content Delivery Network (CDN) is a distributed set of servers that are designed to get your web-based content into the hands of your users as fast as possible. CDNs produce CDN logs that can be analyzed, and this information is invaluable. Why? CDNs host servers all over the world and are designed to help you scale your traffic without maxing out your load balancers. A CDN also gives you added protection against many of the most common cyber attacks. This activity needs to be closely monitored.

To quote the UK National Data Strategy: Splunk is an advanced data platform that delivers right-time analytics from diverse data sets and that enables organisations to ask questions of all their data. It can be used to mitigate cyber security risk, improve performance, increase reliability and observe what is happening in the cloud.

As LogDNA has grown, many of our customers have too, meaning that they are bringing in more ingestion data sources and expanding their use cases for their logs. To help with managing more data, we’re excited to introduce the Control API suite. We’ve built 4 individual APIs that will help companies programmatically configure their data and how they want to ingest logs. Below, we’ll cover each new API in detail as well as why they are massively impactful for our customers.‍

With the advent of IaaS (Infrastructure as a service) and IaC (Infrastructure as Code), it is now possible to manage versioning, code reviews, and CI/CD pipelines at the infrastructure level through resource provisioning and on-demand service routing. Kubernetes is the indisputable choice for container orchestration.

For today’s remote workforce, security professionals need technical security awareness education distinct from the rest of the company’s “don’t click a phishing link” training. Security analysts know how to recognize phishing emails and set secure passwords. However, where does that leave them when it comes to security awareness month?

We recently announced the release of our RemoteWrite SDK to support custom metrics from applications using several different languages – The first SDKs allow shipping of metrics from Golang (Go), Python, Java, Node.js, and.NET. This tutorial will cover the Golang SDK. The SDKs cover not just Logz.io, but can be used by any platform that supports the Prometheus remote write endpoint.

Observability and its implementation may look different to different people. But, underneath all the varying definitions is a single, clear concept: Most software that’s run today uses microservices or loosely coupled distributed architecture. While this design makes scaling and managing your system more straightforward, it can make troubleshooting issues more difficult. The three pillars of observability are different methods to track software systems, especially microservices.

Linux is one of the most popular operating systems today, powering a large portion of the Internet. According to W3Techs, almost half of today’s top-ranked 1 million websites currently run on Linux systems. So, if you want your site—and the application(s) running on it—to be high-performing with lots of uptime, you need to ensure the availability and reliability of your Linux-based servers.

Observability is a term that is becoming commonplace in both startups and enterprises. Log observability is different from monitoring, as it provides visualized metrics from a variety of different systems in a single pane of glass view. This is invaluable for organizations to understand the interdependencies and links between external events and internal performance.

Some things just go better together. Like barbeque and blues, sunsets and beaches, cheese and fine wine — hey, even software and superheroes go better together! That’s why in this blog we are going to look at why IT Operations and Observability just go better together, through a superhero analogy. Enter the Dark Knight himself — Batman! He will represent observability. IT Operations will be represented by Lucius Fox.

There’s an old saying that “there’s no such thing as bad publicity.” Unfortunately, this doesn’t ring true when it comes to data breaches and ransomware attacks. High profile security incidents continue to make headlines, and those headlines are impacting bottom lines. In response to these, the US federal government is modernizing its own cybersecurity infrastructure, and more state governments are implementing laws to protect citizens.

The massive proliferation of log data forces teams to manage the costs to process, route, and store it. Teams need access to this data to gain critical insights into their services, but for many organizations this presents a challenge for their budget. Logging can get expensive, fast, which often results in teams making difficult tradeoffs between aggregating enough logging information to be useful and controlling the cost of storing all those logs.

Apache Cassandra is a distributed database known for its high availability, fault tolerance, and near-linear scaling. It was initially developed by Facebook, but it is a widely used open-source system used by the largest tech companies in the world. There are numerous reasons behind its popularity, including no single point of failure, exceptional horizontal scaling with a data layout designed as a perfect fit for time-series data.

New Relic recently published a survey on the observability projections and trends for the year 2021. At observIQ, an emerging platform of choice in the observability space, we decided to give our users a rundown on what we agreed and disagreed within their survey results.