The latest News and Information on Application Performance Monitoring and related technologies.
Image source: Unsplash.com With an increasing number of applications and populated data, monitoring becomes crucial since businesses are challenged to satisfy millions of users simultaneously. In order to detect performance problems in a timely manner, companies require APM tools to collect and process app and user data that is being generated continuously.
This is Part 2 of a two-part series on Blameless Postmortems. The previous article went into why blameless postmortems are so effective; this second part goes into detail on how to build your own postmortem process and kick it into overdrive. Read Part 1 here. So you've read our first installment and recognized the value of the blameless postmortem for efficiency, culture, and output. Now you're ready to get off the blame train and kickstart a blameless postmortem process of your own. Where to begin?
Today, IT and site reliability engineering (SRE) teams face pressure to remediate problems faster than ever, within environments that are larger than ever, while contending with architectures that are more complex than ever. In the face of these challenges, artificial intelligence has become a must-have feature for managing complex application performance or availability problems at scale.
Getting a good grasp on your application, especially when it is distributed across multiple clouds, kubernetes clusters and serverless functions is not an easy fit.
As the Azure cloud administrator, you need to know who is accessing your cloud resources, how they are access it, what they access, what changed, when they access and from where, etc? Azure AD (Azure Active Directory) provides answers to above by storing the information in two logs, the information stored in them is extremely valuable for troubleshooting, monitoring and for general security related work, the logs are.
