Operations | Monitoring | ITSM | DevOps | Cloud

Are you surprised? Are you under where are the first four tips? I start this series of blog posts on my blog, and you can see and read the previous Best practices, Tips, and Tricks here: And I will be sharing some of them here and others on my blog. So stay tuned for both blogs. Of course, the most recurring task is adding comments to our triggers and actions, but it is always good to know you to delete them. Some of you may be thinking that is a trivial task, simple like adding a comment.

SRE (site reliability engineering) is a discipline used by software engineering and IT teams to proactively build and maintain more reliable services. SRE is a functional way to apply software development solutions to IT operations problems. From IT monitoring to software delivery to incident response – site reliability engineers are focused on building and monitoring anything in production that improves service resiliency without harming development speed.

Read a blow-by-blow analysis of two of the heavyweights in world of DevOps: when should you bet on Terraform, and what calls for Kubernetes?

One more blog topic stemming from our weekly office hours that we hold with the field team here at Shipa. In our last office hours, was asked a question about “what are the difference between DevOps Engineers and SREs?”. Both professions are emerging disciplines and cultures that continue to evolve and play an importance in technology organizations. I’ve been fortunate to have written and spoken about this before; though taking a fresh look at what the two domains try to accomplish.

To supervise the behavior of distributed applications and track the origin of service failures and downtime, developers often use traditional monitoring technologies and tools. However, this approach can fall short in its ability to measure the overall health of modern cloud-native architectures, which can span multiple hosting environments and encompass hundreds of microservices.

In a perfect world, your Configuration Management Database (CMDB) acts as the single source of truth for all your IT device inventory and the relationships between those devices. However, maintaining accuracy is easier said than done. That’s because the traditional method for provisioning and maintaining a CMDB is complex, unwieldy, and outdated the second it's updated. To keep up with the needs of a modern CMDB, an automated discovery and dependency mapping (DDM) solution is a must.

Microservices architecture allows technology companies to build application services around business capabilities. It enables rapid development and also boosts developer productivity. But it also introduces complexity. Troubleshooting and operating an internet-scale application based on microservices is hard. And that’s where distributed tracing comes into the picture. Traditional monolithic application architecture is easy to develop, deploy and monitor.

At ServiceNow, we’ve adjusted to the changing times, encouraging our employees to work in the most efficient and safe ways available to them to embrace the new world of work. We’ve embraced the distributed work model. Three of our global employees are proving it’s possible to adapt to new working environments and thrive—no matter where they are. Their jobs have impacted their personal decisions to work remotely full time, in the office full time, or a hybrid of both.

TechStrong CTO and cofounder Mitch Ashley sat down with Lodewijk Bogaards, Brian Dawson and Cyrille Le Clerc, three industry experts to discuss the present and future of observability. Want to hear everything these experts have to say about the future of about the future of observability?

Apache Kafka is a distributed messaging system that can be used to build applications with high throughput and resilience. It is often used in conjunction with other big data technologies, such as Hadoop and Spark. Kafka-based applications are typically used for real-time data processing, including streaming analytics, fraud detection, and customer sentiment analysis. There are many derivatives such as Confluent Kafka, Cloudera Kafka, and IBM Event Streams.