This week we are excited to officially announce the launch of our new Splunk Community with a re-imagined experience! The Splunk Community first launched nearly eleven years ago and today over 12,000 monthly active members visit our site to engage, learn, answer questions and give back!
Got multiple AWS data sources in the same S3 bucket but struggle with efficient SNS notifications based on prefix wildcards? Well, struggle no more, we’ve got your back. Many of our customers have a centralised S3 Bucket for log collection for multiple sources and accounts. For example, all Config, CloudTrail and Access Log logs may be routed into one central bucket for an organisation.
Evolving business themes come at us in waves. So far this millennium we have had The New Economy, The Cloud, and now Digital Transformation. Underpinning these themes are real, economically significant dynamics that drown out the bleating voices of pundits and cynics alike. The Internet is not a bubble, the cloud is not a fad, and digital transformation is, well, transformative. Let’s take a look at what that last one means for Financial Services.
To understand what’s going on in their environment, DevOps teams usually ship some combination of logs, metrics and traces—depending on which signals they’re hoping to monitor. Each data type will expose different information about what is happening in a system. However, not all of that information will be helpful on a day-to-day basis, which can rack up unnecessary data storage costs. That should require users start to filter telemetry data across their observability stacks.
Logs provide a wealth of information that is invaluable for use cases like root cause analysis and audits. However, you typically don’t need to view the granular details of every log, particularly in dynamic environments that generate large volumes of them. Instead, it’s generally more useful to perform analytics on your logs in aggregate.
Léargas Security (Léargas is Gaelic for “insight”) provides clients with actionable insights into anomalous or abstract behaviors through the correlation of data gathered from converged security controls: cyber and physical.
As a cloud project owner, you want your environment to run smoothly and efficiently. At Google Cloud, one of the ways we help you do that is through a family of tools we call Recommenders, which leverage analytics and machine learning to automatically detect issues and present you with optimizations that you can act on.
When we graduated Loki into a GA release last year, there were more than 137 contributors who already made more than 1,000 contributions to the project. We also added hosted Loki to the lineup of Grafana Cloud offerings after it proved to be stable internally for our ops cluster, storing 40TB and half a trillion log lines each month. There was, however, one persistent problem that kept surfacing, especially for developers who were writing applications in Go: The regex package was slow.
