Even though 2022 hasn’t even ended, Gartner has already charted its top technology trends for 2023. In this blog, we’ll look at how Applied Observability for networks — which Gartner lists as one of the top three trending technology topics in the “optimization” category — helps the organization translate network performance into business performance.

In the world of observability, there are several distinct problems to solve. Fast queries, intuitive visualizations, scalable storage, and more. The technical problems receive the most attention; however, there is another, more subtle problem. How do observability platforms facilitate collaboration on the scale needed by organizations?

The rise of modern applications has kicked basic monitoring tools to the curb. With observability, teams can proactively know, in real-time, what’s happening across the entire stack. Observability allows us to take a holistic view of our IT systems and learn about the current state based on the environment and the data it generates. But how do you properly implement observability? Here are 6 guiding principles to make sure your IT and DevOps teams are set up for success.

On November 16, 2022, I sat down with analyst KellyAnn Fitzpatrick from RedMonk to discuss my favorite topic: observability. This time, we looked at observability in a context of what to do and what to avoid doing as you’re starting and going on an observability journey. Click the image below (or here) for a replay of the session. A machine-generated transcript is available at the end of the post.

A logging framework is a software tool that helps developers output diagnostic information during the execution of a program. This information is used to debug the program or monitor its performance. There are many different logging frameworks available, starting with simple logging libraries to full-fledged logging and observability platforms.

Over the years, as companies have moved from monolith to cloud-native architectures, maintaining high availability has become more challenging. After all, today’s IT ecosystems are complex, distributed and ephemeral, making it increasingly difficult (and, in many cases, downright impossible) for DevOps practitioners and SREs to identify and fix issues manually.

You may get lucky this holiday season with a new 3D printer, either as a gift or something you give yourself as a reward for all your hard work this year. Household 3D printers have made tremendous strides in ease of use and affordability over the last decade.

The structured nature of Kubernetes enables a repeatable and scalable means of deploying and managing services and applications. This has led to widespread adoption across market verticals for both on-premises and cloud deployment models. The autonomous nature of Kubernetes operation, however, demands comprehensive, fully-converged observability and security. This is uniquely possible today using the Elastic platform.

Observability is a growing practice that provides many benefits to IT and DevOps teams. With greater visibility into their environments, teams can determine the state of the system, predict issues, and mitigate them before end users are impacted. Observability makes data more usable and in turn, businesses reap the benefits of having great insights. Are you on the fence on whether to get started with your own observability practice? Check out these 12 observability benefits and get started today!

As organizations strive to meet the challenges of digital transformation, they are adopting newer technologies to build more robust software systems. Next generation observability solutions are paving the way to help them meander this maze to deliver better customer experiences and drive business results.

Configuration Management and Change Tracking are well known key tenets of project management. Change tracking and controlled change ensure that there is a record of the state of a system and if issues arise the cause can be linked to effects. In this blog, I will use a real-world example to demonstrate the importance of configuration and change tracking when it comes to IT observability.

Observability tools have traditionally focused on capturing and analyzing log data to improve application performance monitoring and security. Data observability turns the focus back on the data to improve data quality, tune data infrastructure and identify problems in data engineering pipelines and processes. “Data analysts and business users are the primary consumers of this data,” said Steven Zhang, director of engineering at Hippo Insurance.

As an observability provider, we are always confronted with our clients’ goal for faster resolution of problems and better overall performance of their systems. By working on large-scale projects at Logz.io, I see the same main challenge coming up for all: extracting valuable insights from huge volumes of data generated by modern systems and applications.

We here at Honeycomb really like OpenTelemetry and goodie bags, so we have a nice little OpenTelemetry-flavored holiday goodie bag to share with you before you’re off for the holidays!

In data management, numerous roles rely on and regularly use observability data. The Site Reliability Engineer is one of these roles. Site Reliability Engineers (SREs) work on the digital frontlines, ensuring performant experiences by using observability data to maintain stability and awareness of software running in various environments across organizations.

A hybrid infrastructure brings business benefits but it also brings new challenges. Migrating workloads to the cloud is a complex operation that generates more data than engineering teams can adequately manage. Traditional monitoring tools are limited in helping teams find and fix problems during and after a cloud migration. This can throw business strategies off course, limit customer value and hurt the bottom line.

Is all observability data worth the same cost? If you guessed no, then you’d obviously be correct. Anyone familiar with the very nature of gaining targeted observability knows that some data points hold more value than others. Yet, many observability platforms still treat all types of log data the same, and as a result, related costs remain uniform. One of the most persistent observability challenges today is the cost of indexing log data.

Data has become the most valuable thing in the modern world. According to earthweb, more than 2.5 quintillion bytes of data will be created every day in 2022. These large quantities of data should be appropriately organized to identify meaningful patterns for decision-making. Techniques like clustering are widely used to efficiently collect data into groups based on their similarities and differences and improve the observability of your data.

The line from observability to customer joy is straighter than you think. We recently learned this from NS1, a managed DNS provider and Honeycomb customer, in a panel discussion with Nate Daly, Head of Architecture at NS1 and Chris Bertinato, Software Architect at NS1.

In recent years, there has been a rapid expansion in the API market. Today, every piece of software is either an API or uses one. They are now a crucial component of the modern digital economy. As a result, it is becoming increasingly important to grasp how to examine them carefully. In this article, we'll discuss the definitions of API observability and API monitoring as well as how API observability is better than API monitoring.

Everyday when you come into work, you’re bombarded with a constant stream of problems. From service desk calls to network performance monitoring, you’re busy from the moment you login until the moment you click the “shut down” option on your device. Even more frustrating, your IT environment consists of an ever-expanding set of network segments, applications, devices, users, and databases across on-premises and cloud locations.

The growing popularity of serverless architectures has led to an increased need for solutions to the modern challenges of microservice observability—one of the most critical components for running high-performing, secure, and resilient serverless applications. Observability solutions have to break through the complexity of serverless systems, and with the right stack, observability enables not only fast and easy debugging of applications, but drives optimization and cost efficiency.

To help our customers reduce their overall observability costs, we’re excited to announce the Data Optimization Hub as part of our Open 360™ platform. The new hub inventories all of your incoming telemetry data, while providing simple filters to remove any data you don’t need. Gone are the days of paying for observability data you never use.

Third-party integrations are inevitable in development. Some functionality is outsourced as there is no need to develop something in-house that’s already working, while some integrations are performed to connect with the customer’s ecosystem to help them get their job done better.

Bugs can remain dormant in a system for a long time, until they suddenly manifest themselves in weird and unexpected ways. The deeper in the stack they are, the more surprising they tend to be. One such bug reared its head within our columnar datastore in May this year, but had been present for more than two years before detection.

Observability is the ability to see and understand the internal state of a system from its external outputs. Logs, Metrics, and Traces, collectively called observability data, are external outputs widely considered to be three pillars of observability.

Just like shopping on Black Friday, AWS re:Invent has become a post-Thanksgiving tradition for some of us at Datadog. We were excited to join tens of thousands of fellow AWS users and partners for this annual gathering that features new product announcements, technical sessions, networking, and fun. This year, we saw three themes emerge from the conference announcements and sessions.

Scrum metrics are an essential indicator of your team’s progress. In an agile team, they help you understand the pace and progress of every sprint, ascertain whether you’re on track for timely delivery or not, and more. Although scrum metrics are essential, they are only one facet of the delivery process — sure, they ensure you’re on track, but how do you ensure that there are no roadblocks during development? That’s precisely where observability helps.

As a developer, how much time do you actually spend writing code? According to this survey by The New Stack, developers spend less than one-third (32%) of their time writing new or improving existing code. And this is because they get stuck in multiple tasks that aren’t really part of their main responsibilities.

Observability data is mission-critical for businesses that want to provide stellar customer experiences, remain secure and compliant, and mitigate risk. However, organizations are creating more data as they expand their digital presence. Its increasing volume and complexity have teams looking for solutions that enable them to better control that data, derive more value by making it actionable, and all while keeping their costs under control.