In today's fast-paced digital landscape, the ability to monitor and optimize application performance is crucial for organizations striving to deliver exceptional user experiences. At Elastic, we recognize the significance of providing our user base with a reliable Observability platform that scales with you as you’re onboarding thousands of services that produce terabytes of data each day.

The retail and consumer packaged goods (CPG) industry has undergone significant transformations due to advancements in technology. Technological innovations have reshaped various aspects of the industry, including customer engagement, inventory optimization, and supply chain management. These innovations have helped drive digital transformation, improve operational efficiency, enhance the customer experience, and promote sustainability.

The dynamic nature of the IT landscape poses complex challenges for organizations, necessitating the involvement of observability engineers. These skilled professionals have become indispensable in addressing critical pain points and optimizing system performance. In this blog post, we delve into the challenges observability engineers face and showcase how Mezmo's comprehensive telemetry solution empowers them to overcome these hurdles and achieve optimal results. ‍

The observability landscape is constantly changing and evolving. Despite this, one question often plagues operations leaders: "How can we consolidate disparate data sources and tools to view system performance comprehensively?" These leaders have sought the answer in a single-pane-of-glass solution. However, as Jason Bloomberg and Buddy Brewer discussed in the Mezmo webinar "Solving the Single Pane of Glass Myth," this idea is more myth than reality.

One of the most captivating discussions I had at KubeCon Europe 2023 in Amsterdam was about standardization of a query language for observability. This query language standard aims to provide a unified way of querying observability data across logs, metrics, traces, and other relevant signals. The conversation shed light on the pressing need for a standardized approach to overcome the challenges posed by the plethora of query languages currently in use.

On July 4th we celebrate. We celebrate freedom of movement, freedom of assembly, removal of excessive taxation, and much, much more. But what about digital independence? Removing the tyrannical yoke of control over your observability data. Authoritarian vendors restrict access and movement; they dictate proprietary formatting and even limit what can be commingled with your data, then apply enormous tax burdens (i.e. license fees) just to store your data.

With various open source platforms on the market, engineers have to make smart and cost-effective choices for their teams in order to scale. Elastic Cloud, and its flagship product Elasticsearch, are one of several options available, but how do they compare to a full-stack observability platform like Coralogix? This article will provide a complete breakdown between Coralogix and Elastic Cloud, from essential industry features, like logs, metrics and traces, to pricing models and support services.

In my previous blog post, I discussed how transitioning from legacy monolithic applications to microservices based applications running on Kubernetes brings a range of benefits, but that it also increases the application’s attack surface. I zoomed in on creating security policies to harden the distributed microservice application, but another key challenge this transition brings is observing and monitoring the workload communication and known and unknown security gaps.

Asaf and I founded Logz.io in 2015 to provide developers with the ultimate open source log management experience. With our product, logging with the ELK Stack was simple, efficient, and automated for the first time – so customers could save engineering costs and accelerate MTTR.

In this post I will introduce sysgrok, a research prototype in which we are investigating how large language models (LLMs), like OpenAI's GPT models, can be applied to problems in the domains of performance optimization, root cause analysis, and systems engineering. You can find it on GitHub.

By storing copies of your content in geographically distributed servers, content delivery networks (CDNs) enable you to extend the reach of your app without sacrificing performance. CDNs lessen the demand on individual web hosts by increasing the number and regional spread of servers that are able to respond to incoming requests for cached content. As a result, they can deliver web content faster and provide a better experience for your end users.

Looking to get started with log aggregation? Or perhaps take your logging game to a whole new, more advanced level? You’ve come to the right place. Grafana Loki is a key component of Grafana Labs’ open and composable Grafana LGTM stack (Loki for logs, Grafana for visualization, Tempo for traces, Mimir for metrics).

As our customers share their frustrations with the volume and growth of their observability data, we’ve got our eyes set on making it easier to manage. Our Spring 4.1 Launch involved enhancements to the Cribl suite of products — Cribl Stream, Cribl Edge, and Cribl Search — that give users more choice and control over their end-to-end observability architecture.

Arguably, OpenTelemetry exists to (greatly) increase usage of tracing and metrics among developers. That said, logging will continue to play a critical role in providing flexible, application-specific, event-driven data. Further, OpenTelemetry has the potential to bring added value to existing application logging flows.

Logz.io is honored to have our Cloud SIEM as one of the products available as part of the launch of AWS AppFabric. For customers invested in AWS, this inclusion allows you to use our cloud-based, agile SIEM seamlessly alongside other critical SaaS applications.

You may be thinking of investing in multiple cloud vendors to increase redundancy and deal with the complexity of your enterprise requirements. You are not alone. Many enterprises are moving in this direction to take advantage of the options offered by competing cloud vendors. Adopting one major cloud vendor is a complex project that can consume a company for months if not years.

In my previous articles, I discussed how to design considerations for observability solutions and how observability can augment your security implementation. In this article, I will discuss how an observability solution can provide valuable insights into your business operations through the collected data from various systems, applications, and services.

Today we’re happy to announce our new open source, scalable logging solution, VictoriaLogs, which helps users and enterprises expand their current monitoring of applications into a more strategic ‘state of all systems’ enterprise-wide observability. Many existing logging solutions on the market today offer IT professionals a limited window into live operations of databases and clusters.

Over the last decade, log management has been largely dominated by the ELK Stack – a once-open source tool set that collects, processes, stores and analyzes log data. The ‘k’ in the ELK Stack represents Kibana, which is the component engineers use to query and visualize their log data stored in Elasticsearch. Sadly, in January 2021, Elastic decided to close source the ELK Stack, and as a result, OpenSearch was launched by AWS as an open source replacement.

Easy to implement, effective data management tools that provide fast time to value are the exception rather than the rule, and top-notch support for those tools is also hard to come by. That’s why Cribl prioritizes creating products that make the lives of engineers and systems admins as easy as possible. The reviews on Gartner Peer Insights give us a glimpse into how well we’re holding up our end of the bargain.

AWS Fargate is a serverless pay-as-you-go engine used for Amazon Elastic Container Service (ECS) to run Docker containers without having to manage servers or clusters. The goal of Fargate is to containerize your application and specify the OS, CPU and memory, networking, and IAM policies needed for launch. Additionally, AWS Fargate can be used with Elastic Kubernetes Service (EKS) in a similar manner.

The Cribl team just wrapped up the 2023 AWS Summit in Washington, DC, and we were thrilled to spend a few days chatting with public sector organizations looking to gain the freedom and flexibility our products offer.

OpenTelemetry (also abbreviated as OTEL) is an increasingly popular open-source observability platform under the Cloud Native Computing Foundation (CNCF), which is currently the most active project in the CNCF after Kubernetes. It was created to establish a unified and vendor-agnostic way for instrumenting, collecting, and exporting telemetry data for your system and application across traces, logs, and metrics.

In this livestream, Jackie McGuire and I discuss the harmful effects of data debt on observability and security teams. Data debt is a pervasive problem that increases costs and produces poor results across observability and security. Simply put — garbage in equals garbage out. We delve into what data debt is and some long term solutions. You can also subscribe to Cribl’s podcast to listen on the go!

Fintech companies operate in a complex technological and regulatory environment. They rely heavily on cloud-native technologies and microservices architectures to handle financial transactions and data, often at a massive scale. To maximize application reliability, fintech companies need full visibility into their software systems and applications. An agile monitoring solution like observability is crucial to improving performance and user experience.

Cribl has a unique position right in the middle of the observability market, giving us a distinct view of all things security, APM, and log analysis. Observability as a concept has exploded into specialized areas over the past two years, and making sense of the players and market forces, particularly in a difficult macro environment, can be tricky. Let’s break it down.

We recently launched several new Cloud Monitoring features to improve your visualization and troubleshooting experience.

The State of DevOps Report finds a clear link between documentation quality and an organization’s ability to meet its performance goals.

If you work with large amounts of log data, you know how challenging it can be to analyze that data and extract meaningful insights. One way to make log analysis easier is to normalize your log messages. In this post, we’ll explain why log message normalization is important and how to do it in Graylog.

Log Management tools are crucial for the security and performance of your IT infrastructure. With the right log management system, you can quickly detect and respond to any anomaly or performance issue. Presently, there are numerous log management platforms. Each with its own unique set of features and benefits. While most of these platforms offer industry-standard capabilities, what sets them apart from each other are the stand-out features, pricing, and overall user experience.

On June 28th I will be hosting a webinar, ‘The Fundamentals of Searching Observability Data’. So why should you attend? Because things have, and will continue to change in the way we manage the IT data collected across the enterprise. A recent study shows that enterprises create over 64 zettabytes (ZB) of data, and that number is growing at a 27 percent compound annual growth rate (CAGR). The scary part?

The basic goal of log management is to make log data easy to locate and understand so that users can identify how their services are performing and troubleshoot more quickly. Logging as a Service, or LaaS, takes log management a step further by providing a solution that seamlessly scales and manages your log data via cloud-native architecture.

Distributed microservices and cloud computing have been game changers for developers and enterprises. These services have helped enterprises develop complex systems easily and deploy apps faster. That being said, these new system architectures have also introduced some modern challenges. For example, monitoring data logs generated across various distributed systems can be problematic.

Today’s IT systems are ever more fragmented. It is commonplace to see polyglot systems, written in multiple programming languages, and using a plethora of tools and cloud services as infrastructure building blocks, whether data stores, web proxy or other functions. In this dynamic cloud-native realm, open standards and open specifications have become integral drivers of compatibility, collaboration, and convergence – the Three C’s of Open Standards, if you will.

IT, DevOps, and security teams are figuring out the best ways to manage their complex, ever-growing, ever-changing environments. And one contributing factor to all the complexity is the rise of using multiple cloud services. One cloud service to manage is difficult enough, but adding more to the mix — each with its own interface and set of tools — makes everyone’s job significantly more difficult.

Any web-based business must have effective log monitoring in place to guarantee the efficient operation of its applications and systems. Tools for log monitoring are essential for error detection, performance analysis, and problem-solving. The top five log monitoring tools will be examined in this post, along with their features, prices, advantages, and disadvantages.

In Elasticsearch 8.8, we’re introducing the reroute processor in technical preview that makes it possible to send documents, such as logs, to different data streams, according to flexible routing rules. When using Elastic Observability, this gives you more granular control over your data with regard to retention, permissions, and processing with all the potential benefits of the data stream naming scheme. While optimized for data streams, the reroute processor also works with classic indices.

Logging provides a wealth of information about system events, errors, warnings, and activities. When troubleshooting issues, logs can be invaluable for identifying the root cause of problems, understanding the sequence of events leading to an issue, and determining the necessary steps for resolution. By regularly analyzing logs, administrators can identify performance bottlenecks, resource limitations, and abnormal system behavior.

Classless Inter-Domain Routing (CIDR) is the dominant IP addressing scheme in the modern web. By enabling network engineers to create subnets that encapsulate a set range of IP addresses, CIDR facilitates the flexible and efficient allocation of IPs in virtual private clouds (VPCs) and other networks.

Gaming apps are complex systems. They combine multi-function systems, like the game engine, to other resources such as server containers, proxies and CDNs in order to give users a real-time interactive experience. At the same time, managing cross-functional behavior also means that games could generate massive amounts of data, commonly known as logs. You’ll want to turn that data into useful information to help improve game performance.

A reference architecture is a lovely document, but they rarely help engineers and architects implement their tools effectively. Most reference architectures offer plenty of suggestions and ideas, but not enough context. We will explore ways to make reference architectures more useful while reducing reliance on the vague and dreaded “It Depends. Cribl has just released its first official reference architecture.

Coralogix supports logs, metrics, traces and security data, but some organizations need a multi-vendor strategy to achieve their observability goals, whether it’s developer adoption, or vendor lock-in is preventing them from migrating all of their data. Coralogix offers a set of features that allow customers to bring all of their data into a single flow—across SaaS and hosted solutions.

Late last year we announced an early access program for Grafana Cloud Logs Export, a feature that allows users to easily export logs from Grafana Cloud to their own cloud-based object storage for long-term archival purposes. We are pleased to announce that the feature is now in public preview for all Grafana Cloud users, including those on the Free tier!

Picture a simple E-commerce platform with the following components, each generating logs and metrics. Imagine now the on-call Engineer responsible for this platform, feet up on a Sunday morning watching The Lord of The Rings with a coffee, when suddenly the on-call phone starts to ring! Oh no! It’s a customer phoning, and they report that sometimes, maybe a tenth of the time, the web front end is returning a generic error as they try to complete a workflow.

Data explosion is prevalent and impossible to ignore in today’s business landscape, with organizations face a pressing challenge: the ever-increasing volume of log data. As applications, systems, and services generate a torrent of log entries, it becomes crucial to find a way to navigate this sea of information and extract meaningful value from it. How can you turn the overwhelming volume of log data into actionable insights that drive business growth and operational excellence?

In the era of big data, data lakes have emerged as a popular way to store and process massive amounts of data. Fortunately, with Cribl Search and Cribl Stream, you can create a Data Loop to optimize the use of your data lake by saving Search results as part of an investigation. Our four-part video series explains how to set up Cribl Search and Cribl Stream to establish a Data Loop using the Amazon S3 Data Lake destination in Cribl Stream and the Cribl Stream in_cribl_http source.

CI/CD pipelines have become a cornerstone of agile development, streamlining the software development life cycle. They allow for frequent code integration, fast testing and deployment. Having these processes automated help development teams reduce manual errors, ensure faster time-to-market, and deliver enhancements to end-users. However, they also pose risks that could compromise stability of their development ecosystem.

SIEM has been a crucial component of security systems for nearly two decades. While there’s ample information on operating SIEM solutions out there, guidance on evaluating and managing them effectively is lacking. We’ve noticed many SIEM vendors are taking advantage of this dearth of knowledge and not providing customers with needed value for what they’re buying.

In the complex world of Kubernetes, logs serve as the backbone of effective monitoring, debugging, and issue diagnosis. They provide indispensable insights into the behavior and performance of individual components within a Kubernetes cluster, such as containers, nodes, and services.

Log data is the most fundamental information unit in our XOps world. It provides a record of every important event. Modern log analysis tools help centralize these logs across all our systems. Log analytics helps engineers understand system behavior, enabling them to search for and pinpoint problems. These tools offer dashboarding capabilities and high-level metrics for system health. Additionally, they can alert us when problems arise.

While log parsing isn’t very sexy and never gets much credit, it is fundamental to productive and centralized log analysis. Log parsing extracts information in your logs and organizes them into fields. Without well-structured fields in your logs, searching and visualizing your log data is near impossible.

In today’s digital-first world, most security problems are actually data problems, and data volumes are outpacing organizations’ abilities to handle, process, and get value from it. You’ll have 250% more data in five years than you have today, but the chances of your budget increasing to match that are slim. The challenges that come with managing the rise in enterprise data volume directly affect your ability to adequately address cybersecurity risks.

Are you prepared for the unexpected? In today's rapidly evolving world, operational resilience has never been more critical for businesses to survive and thrive. Resiliency is the ability of a system to maintain its operations under adverse conditions, including system failures, unexpected surges in user demand, or even security breaches. The heart of many applications, particularly in this era of data-driven decision-making, is the data store or database.