Tag-based metrics are typically used by IT operations and DevOps teams to make it easier to design and scale their systems. Tags help you to make sense of metrics by allowing you to filter on things like host, cluster, services, etc. However, knowing which tags to use, and when, can be confusing. For instance, have you ever wondered about the difference between intrinsic tags (or dimensions) and meta tags with respect to custom application metrics? If so, you’re not alone.

Welcome to autumn! Here in New England we anxiously await the transition to winter as our leaves start to change color and the summer heat begins to temper. It’s been a busy few seasons for us – we’ve introduced new features and launched our new community efforts. Want to learn more? Read on!

This year, at Sumo Logic’s third annual user conference, Illuminate 2018, we presented Sumo Logic Notebooks as a way to do data science in Sumo Logic. Sumo Logic Notebooks are an experimental feature that integrate Sumo Logic, notebooks and common machine learning frameworks. They are a bold attempt to go beyond what the current Sumo Logic product has to offer and enable a data science workflow leveraging our core platform.

This week at the Microsoft Ignite, we unveiled two new Sumo Logic applications for Microsoft Azure services — Azure SQL Database and Azure Active Directory — and two new native integrations with Azure Monitor and Blob Storage. As a cloud-native company, our goal at Sumo Logic is to give our customers the flexibility to create digital IT and DevOps initiatives that leverage multi-cloud deployments in Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure.

ITOA becomes very manageable and simple with XpoLog log management tool. This post will focus on methods the team here at XpoLog uses to manage unstructured IT log data and visualization using smart tagging techniques.

If you’ve been paying attention, you know that although collecting and reviewing metrics and logs is a core part of running a stable and successful service, access to raw events and the ability to search and pivot on any dimension of your production environment, no matter how high-cardinality, is what will help your team debug and troubleshoot new problems and outages more quickly.

Achieving optimal performance can be challenging when you depend on separate platforms to monitor service health and to manage your logs. When data about your systems is spread across multiple platforms, investigating issues—and ultimately resolving them—takes longer and requires expertise with more tools. It takes more effort to identify real customer impact, as well as to verify that your responses to an incident are having the desired effect.

In today’s hyper-connected world, a company’s differentiation is completely dependent upon delivering a better customer experience, at scale, and at a lower cost than the competition. This is no easy feat, and involves a combination of many things, particularly adopting new technologies and architectures, as well as making better use of data and analytics.

Imagine that you are tasked with architecting your mission-critical cloud application. Or migrating your on-premises app to the cloud. And you ask yourself “how do the cloud savvy companies like Twitter, Airbnb, Adobe, SalesForce, etc. build and manage their modern applications?”