Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.

RCA Series: Root Cause Analysis in Manufacturing, Electric Grids & Connected Devices (4/4)

With digitization adopted in many industries, real-time data from manufacturing and operational equipment can be used to monitor and optimize operation - by applying data-driven modeling including machine learning. Learn how you can ingest sensor data from industrial processes and operational equipment into Elastic, build monitoring dashboards and set up automated alerts in Kibana, and apply predictive modeling to optimize your operations (OT).

RCA Series: Accelerate security investigations w/ machine learning and Elastic (3/4)

Comprehensive security requires multiple layers of threat protection. Sophisticated threats exploit idiosyncrasies in your environment. Unsupervised machine learning identifies patterns of normal activity from your data, and therefore can catch attacks that standard approaches to threat hunting, such as pre-defined rules, are likely to miss. This video explains how machine learning adds a layer to your threat protection, and how interactive tools offered in the Elastic Security solution accelerate the investigation of security incidents.

RCA Series: Root Cause Analysis in Observability with Elastic AIOps (2/4)

Root cause analysis empowers you to prevent issues from recurring that were revealed by your monitoring IT systems and online applications including eCommerce sites. See Elastic engineers walk you through applying four AIOps capabilities and accelerate MTTR by automatically categorizing logs, explaining log rate spikes, visually inspecting anomalous components in their context, and correlating slow or failed transactions with potential root causes.

Kentique - The Fragrance of Observability

What does observability smell like? For ages, humans have relied on sight to observe network, cloud, container, and data center traffic. Kentik is proud to usher in the next step in the evolution of network observability. Welcome to the age of scent! Introducing Kentique, a new fragrance from the creators of Kentik. Whether managing network infrastructure in the server room or entertaining guests at an AWS re:Invent afterparty, you need to smell the part – an aromatic infusion of routers, clouds, clusters, and telemetry dancing in a symphony of observability.

Using Practical Alerting to Stay on Top of Teams Call Quality - Part 2

Running your business using Teams isn’t without its challenges. We already did a post here about some of the Microsoft Teams alerts IT teams need to be alerted to sooner rather than later. But, because of how complex large Teams setups are, we’ve got a few more to add to the collection. Today, we’re focusing on the Microsoft-specific challenges you might face.

Code Instrumentation in Cloud Native Applications

Cloud native is the de facto standard approach to deploying software applications today. It is optimized for a cloud computing environment, fosters better structuring and management of software deployments. Unfortunately, the cloud native approach also poses additional challenges in code instrumentation that are detrimental to developer productivity.

Ensure release safety with feature flag tracking in Datadog RUM

Developers and teams who want to deploy new code often and safely leverage feature flags to decouple code deployments from feature releases. Feature flags enable teams to release new features to a subset of users, making it possible to test a new feature’s impact on users and ensuring that developers can easily roll back the feature if it causes downstream issues.

The Future of Website Development: Exploring the Impact of Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are two cutting-edge technologies that are revolutionizing the field of website development. AI refers to the ability of computers to perform tasks that typically require human intelligence, such as recognizing speech, understanding natural language, and making decisions based on data. On the other hand, ML is a subset of AI that involves training algorithms to learn from data and make predictions or decisions based on that learning.

Observability, Meet Natural Language Querying with Query Assistant

Engineers know best. No machine or tool will ever match the context and capacity that engineers have to make judgment calls about what a system should or shouldn’t do. We built Honeycomb to augment human intuition, not replace it. However, translating that intuition has proven challenging. A common pitfall in many observability tools is mandating use of a query language, which seems to result in a dynamic where only a small percentage of power users in an organization know how to use it.