Operations | Monitoring | ITSM | DevOps | Cloud

Today, we made an important announcement that builds on and extends our category leadership in Continuous Intelligence. This blog is to put that announcement into context for our customers, partners and the broader industry. In 2017, we shared our strategy to democratize machine data by building a cloud-native, Continuous Intelligence Platform™ to 1) unify modern application and machine data; 2) support the ongoing proliferation of users and access; and 3) leverage cloud-scale economics.

As more and more applications move to the cloud, the complexity of application architectures inevitably increases. It is a burden we willingly take on because the benefits—flexible deployment, technology diversity, independent scaling, and much more— tend to far outweigh the costs. But along this transition, most organizations face a dilemma, to divert resources to the necessary tooling for effective monitoring and troubleshooting of these systems – i.e.

Automation is a key component in the management of the entire software release lifecycle. While we know it is critical to the Continuous Integration/Continuous Delivery process, it is now becoming equally essential to the underlying infrastructure you depend on. As automation has increased, a new principle for managing infrastructure has emerged to prevent environment drift and ensure your infrastructure is consistently and reliably provisioned.

Sumo Logic is taking the next step towards Moderate authorization under the Federal Risk and Authorization Management Program (FedRAMP). Today Sumo moved from “Ready” to “In Process” on the FedRAMP Marketplace, an important step toward expanding our ability to serve U.S. governmental agencies. Sumo Logic expects to achieve FedRAMP-Moderate Authorization quickly while working closely with the Federal Trade Commission (FTC) as our agency partner.

I am spending a considerable amount of time recently on distributed tracing topics. In my previous blog, I discussed different pros and cons of various approaches to collecting distributed tracing data. Right now I would like to draw your attention to the analysis back-end: what does it take to be good at analyzing transaction traces?

There has been increasing buzz in the past decade about the benefits of using a microservice architecture. Let’s explore what microservices are and are not, as well as contrast them with traditional monolithic applications. We’ll discuss the benefits of using a microservices-based architecture and the effort and planning that are required to transition from a monolithic architecture to a microservices architecture.

Today’s organizations have the challenge of managing several different applications and software within their technology stack. The more public-facing platforms an organization utilizes, the greater their public attack surface risks. Without proper protection, they and their community can become an easy target for malicious actors.

Technology has a way of circling around to the same ideas over time, but with different approaches that learn from previous iterations. Service Oriented Architecture (SOA) and Microservices Architecture (MSA) are such evolutionary approaches. Where lessons learned made sense, they were reused; and where painful lessons were learned, new methods and ideas were introduced.

We’re excited to announce the first version release of our new dashboard framework: Dashboard (New). Built on top of a scalable, flexible, and extensible charting system, the new dashboards provide customers with deep control over their visuals, enable metadata rich workflows, and create dashboards in a dashboard first GUI.

Compared to even just a few years ago, the tools available for data scientists and machine learning engineers today are of remarkable variety and ease of use. However, the availability and sophistication of such tools belies the ongoing challenges in implementing end-to-end data analytics use cases in the enterprise and in production.