IBM Cloud Log Analysis with LogDNA enables you to quickly find the source of issues and gain deeper insight into application and cloud environment data. IBM Cloud logging begins with log aggregation from application and services within IBM Cloud. IBM partners with LogDNA to bring collection, log tailing and blazing fast log search. LogDNA supports integrations to many cloud-native runtimes and environments.
We’ve all experienced it – executives with unrealistic expectations who vastly underestimate the amount of time our work can take. Most of us assume that to be the exception and not the norm. But when it comes to monitoring and troubleshooting, that seems to be the all too commonplace.
What a pleasure it was to see many of our customers at our Illuminate user conference, September 11-12. We had record attendance from customers, influencers, and partners. Our time was packed with keynotes, customer presentations (35 customer breakout sessions), certifications, sharing best practices, and time networking and having fun together.
Jim Barksdale, former CEO of Netscape, once said “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” While Jim may have said this in jest, the exponential boom in data collection indicates that we increasingly prefer to rely on facts rather than conjecture when making business decisions. More data yields greater insights about customer preferences and experiences, internal processes, and security vulnerabilities — just to name a few.
ELK and Kubernetes are used in the same sentence usually in the context of describing a monitoring stack. ELK integrates natively with Kubernetes and is a popular open-source solution for collecting, storing and analyzing Kubernetes telemetry data. However, ELK and Kubernetes are increasingly being used in another context — that of a method for deploying and managing the former.
Everything old is new again–that’s a universal law! In our industry, we have talked about Service Level Agreements (SLAs) for a long time. At Sumo, we are proud to continue to be the only Machine Data Analytics Platform as a Service that commits to a query performance SLA.
Log file parsing is the process of analyzing log file data and breaking it down into logical syntactic components. In simple words - you’re extracting meaningful data from logs that can be measured in thousands of lines. There are multiple ways to perform log file parsing: you can write a custom parser or use parsing tools and/or software.
Massively scaling free-text search has always been the holy grail in big data. Many software firms now face the burgeoning challenge of searching through previously untapped data sources and the current trend is far surpassing the petabyte scale. Here at LogDNA we manage free-text search for thousands of customers with distinct traffic profiles across a multitude of Elasticsearch clusters.
Log files in Linux often contain information that can assist in tracking down the cause of issues hampering system or network performance. If you have multiple servers or levels of IT architecture, the number of logs you generate can soon become overwhelming. In this article, we’ll be looking at some ways to ease the burden of managing your Linux logs.
Every log management solution out there has its own alerting feature. Alerts are a critical component of every logging tool. They can tell you whether an event is something you want to check out rather than just normal everyday activity you want to ignore. Graylog’s simplified interface is incredibly accessible to assist you with all the information you need in real-time, yet scalable enough to never compromise the level of detail provided.
