Over the years, automation has become a key component in the management of the entire software release lifecycle. Automation helps teams get code from development into the hands of users faster and more reliably. While this principle is critical to your source code and continuous integration and delivery processes, it is equally essential to the underlying infrastructure you depend on.
It’s becoming increasingly harder to manage the volume of threats coming into enterprise networks as attackers become more sophisticated, the threat landscape expands and enterprises continue to adopt modern applications at cloud scale.
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.
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.