Red Hat OpenShift is an open source container application platform that incorporates a collection of software that enables developers the ability to run an entire Kubernetes environment. It includes streamlined workflows to help teams get to production faster and is tested with dozens of technologies while providing a robust tightly-integrated platform supported over a 9-year lifecycle.

Anyone tracking the evolution of the IT industry is probably familiar with the concept of Industry 4.0. Essentially, it describes the process by which traditional industrial tasks become both digitized and continually managed in an IT-like fashion via modern technologies like cloud computing, digital twins, Internet of Things (IoT) sensorization, and artificial intelligence/machine learning.

People’s involvement has been inevitable with log management despite advancements in ITOps. Log management at a high level collects and indexes all your application and system log files so that you can search through them quickly. It also lets you define rules based on log patterns so that you can get alerts when an anomaly occurs. Log management analytics solution leveraging RDA has been able to detect anomalies and aid predictive models over a machine learning layer.

First of all, don’t ask this! Instead of asking what to log, we should start by asking “what questions do we want to answer?” Then, we can determine which data needs to be logged in order to best answer these questions. Once a question comes up, we can answer it using only the data and knowledge that we have on hand. In emergent situations such as an unforeseen system failure, we cannot change the system to log new data to answer questions about the current state of the system.

It sounds like a wild claim, considering that billion dollar companies like Splunk, Datadog, New Relic, and Solarwinds are consistently making national headlines, for both good and bad reasons. Observability leaders are anything but invisible, so how can the perfect solution be different? Are they that far off?

There is nothing worse than waking up to an angry customer complaining that your website is failing to accept their payment at checkout. This may be worrying for some since payments not being processed can be equivalent to losing money; however with Tag Spotlight, this should be a relatively quick problem to dissect. The key question here is whether this is an issue that all our customers are facing or an isolated event.

Troubleshooting cloud services and infrastructure is an ongoing challenge for organizations of all sizes. As organizations adopt more cloud services and their cloud environments grow more complex, they naturally produce more telemetry data – including application, system and security logs that document all types of events. All cloud services and infrastructure components generate their own, distinct logs.

We are excited to announce that Elastic is joining forces with Cmd to accelerate our efforts in Cloud security - specifically in cloud workload runtime security. By integrating the capabilities of Cmd's expertise and product into Elastic Security, we will enable customers to detect, prevent, and respond to attacks on their cloud workloads.

A WS is a comprehensive platform with over 200+ types of cloud services available globally. As organizations adopt these services, monitoring their performance can seem overwhelming. The majority of AWS workloads behind the scenes are dependent on a core set of services: EC2 (the compute service), EBS (block storage), and ELB (load balancing).

LogDNA integrates with your PagerDuty instance to help trigger incidents based on log data coming in from your ingestion sources. This allows your teams to quickly understand when there are issues with your application, and where in the logs you can investigate to understand root cause. To help further accelerate your team’s ability to understand the state of your applications, we are introducing the ability to automatically resolve those PagerDuty Incidents directly from LogDNA.