Another day starting up your laptop or workstation, logging into programs, and waiting for that first call to come in. As an IT help desk analyst, you love when you can solve people’s problems, but sometimes the number of calls feels overwhelming. Although each analyst tier responds to different customer or employee concerns, you all share the same basic job functions like answering calls, asking questions, and research answers.
If you’re just getting familiar with full-stack observability and Coralogix and you want to send us your metrics and traces using the new OpenTelemetry Community Demo Application, this blog is here to help you get started. In this simple, step-by-step guide, you will learn how to get telemetry data produced by the OpenTelemetry Demo Webstore into your Coralogix dashboard using Docker on your local machine.
One tool for all your Python performance tracking needs We're building this neat service in Python to ingest data in Elasticsearch from various sources (MySQL, Network Drive, AWS, etc.) for Enterprise Search. Sucking data from a third-party service to Elasticsearch is usually an I/O-bound activity. Your code sits on opened sockets and passes data from one end to the other. That's a great use case for an asynchronous application in Python, but it needs to be carefully crafted.
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?
OpsRamp has enhanced its hybrid observability capabilities by adding an integrated log management solution to unify log, event and alert data within customers’ monitoring and event management command center. Presenting this log data as part of a unified view of IT performance data and integrating it with remediation capabilities will allow enterprises and service providers to expedite the process of identifying and resolving potential issues before they impact their business operations.
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.
Data processing involves collecting, organizing, and manipulating data in a systematic manner in order to extract useful information from it. It involves a series of steps that are performed on a set of data to transform it into a more meaningful and functional form for a specific purpose. Starting from collecting the data to the end part of processing it, data undergoes several layers of checks and balances before it is let out as we see it.
The gaming industry delivers specialized software at scale to users who expect a flawless interface. Application performance monitoring (APM) will measure critical software performance parameters using telemetry data. By monitoring this data, teams can ensure their game delivers the best user experience and quickly detect when the software needs updates to fix errors or meet key performance indicators (KPIs).
Grafana Agent v0.30 is here! The past couple of Grafana Agent releases have been pretty exciting for us. We introduced Agent Flow as a new way to configure, run, and debug telemetry pipelines. We also announced OpenTelemetry Collector components to expand on our Big Tent philosophy and allow users to switch seamlessly between the Prometheus and OTel ecosystems. This latest release continues that momentum by introducing Loki components for building logging pipelines and marking Flow mode as beta!
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.
Log files and system logs have been a treasure trove of information for administrators and developers for decades. But with more moving parts and ever more options on where to run modern cloud applications, keeping an eye on logs and troubleshooting problems have become increasingly difficult.
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.
The community has spoken, six Helm charts is not enough! We agree! In all seriousness though, six charts is simply too many to maintain. And while it might sound counterintuitive, that’s why we are announcing a new Helm chart. By focusing on the “Grafana Labs way" to run Grafana Loki using Helm, we believe this will help us and the community concentrate our Helm efforts into a single chart. This new chart is released under grafana/loki at Helm version 3 or higher.
We’re pleased to announce that Elastic has been named a Leader in the 2022 Gartner® Magic Quadrant™ for Insight Engines. This is our second year of inclusion in the Gartner Magic Quadrant for this category, and this year’s evaluation places Elastic as the furthest entry on the "Completeness of Vision" axis.
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’re excited to announce significant improvements to our Archive+Restore capabilities – which enables low-cost long term log storage in AWS S3 or Azure Blob, while providing access to ingest those logs into Logz.io at any time. The first enhancement is Power Search, which will make it faster to restore logs from archived log data in AWS S3 (and soon for Azure Blob) in our Open 360™ platform.
We are excited to announce that Elastic has been recognized as a Strong Performer in The Forrester Wave™: Artificial Intelligence for IT Operations (AIOps), Q4 2022 in our first year participating! As organizations modernize their infrastructure and applications, operations and development teams are faced with an exponential growth in data.
Fact: The Security Operations team at Grafana Labs loves logs. They are a key pillar of observability for many reasons, such as how they are stuffed full of details to help us diagnose the “why?” when things go wrong. This is especially true when the information pertains not to a series of unfortunate events, but instead to an adversary trying to cause us harm.
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.
Resilience has become the new strategic imperative for manufacturers during these testing times. As the world’s challenges make headlines, so do the innovative responses of manufacturing leaders. Savvy manufacturers automate, overhaul fundamental processes, modernize their security posture and reduce their CO2 footprint. Forward-focused organizations double down on their cloud investment to become more agile and resilient. And none of it is possible without data.
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.
You’ve made the decision to implement a centralized log management solution because you know that it’s going to save you time and money in the long term. However, to get the most bang for your log management buck, you need to understand how the different parts of your log management deployment work. Once you understand each resource, you can implement a more efficient log management architecture.
Knowing the differences between data warehouses and databases can clear up a lot of confusion for many people, especially with the volume of data we have these days. In this blog post, I'll discuss the differences between these two types of data systems. I'll also provide some examples to help illustrate the points we make. Let's get started! (This article was written by Austin Chia.)
Elastic Universal Profiling is based on technology that came into Elastic as part of the acquisition of optimyze.cloud — a startup that had developed Prodfiler.com, the world’s first frictionless fleet-wide in-production multi-runtime profiler that was launched in August 2021. In order to bring the vision of frictionless deployability, low performance overhead, “just run it everywhere” magic to the broader market, a number of technical innovations were necessary.
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.
While developing elmah.io support for WPF, I had the chance to look into WPF for the first time in many years. I couldn't stop myself from digging down into all sorts of details about how logging has evolved in WPF since I last wrote a WPF app. In this post, I'll share some of the findings I made in this rediscovering journey.
Organizations have different data lakes they use to search, whether it is Splunk, Qradar, or Sumo Logic just to name a few. Exabeam (UEBA Advanced Analytics) sits on top of those existing data lakes and pulls specific sources by running continuous queries every few minutes into Exabeam. The image below shows a Splunk query to pull windows event logs into Exabeam Advanced Analytics over the port (8089). The query is complex.
We are excited to announce the release of Graylog 5.0! Graylog 5.0 brings updates across our entire product line, including changes to infrastructure, Security, Operations, and our Open offerings. For more detailed information on what’s changed, visit our changelog pages for Graylog Open and Graylog Operations/Graylog Security.
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.
Grafana Loki 2.7 has arrived! With it comes an experimental feature we are rather excited about: a redesigned index based off of the Prometheus TSDB index. While we are still in the early stages, this enhancement in Grafana Loki, which we previewed at ObservabilityCON 2022, creates a smaller storage footprint, better query performance, and much more that we will dive into below!
Today we’re excited to announce Logz.io Telemetry Collector – an agent that can send logs, metrics, and traces to Logz.io in a single installation as part of our Open 360™ platform. With Telemetry Collector, customers can get started monitoring their services with Logz.io faster than ever by simplifying the data collection process.
