We are excited to join AWS for the launch of Amazon CloudWatch Metric Streams; a fully managed, scalable, and low latency service that streams Amazon CloudWatch metrics to partners via Amazon Kinesis Data Firehose. AWS and Sumo Logic customers can now leverage AWS Kinesis Firehose for Metrics Source for streaming CloudWatch metrics into their Sumo Logic accounts, to help simplify the monitoring and troubleshooting of AWS infrastructure, services, and applications.

Microservices and serverless are both important topics in the world of cloud-native computing. Yet, although serverless functions and microservices architectures often go hand-in-hand, they’re distinct technologies that fill different roles in modern software environments. Here’s an overview of what microservices and serverless are, how they relate to each other, how they are different, and why you may or may not wish to deploy a serverless microservice.

Cloud-native and serverless come hand in hand. One of the initial motivations to move business workflows to the cloud was related to cutting costs related to provisioning infrastructure and elasticity that on-demand allocation of resources is offering.

Over a year ago we decided to invest heavily in Application Observability, understanding the modern observability platform must unite logs, metrics, and traces in one analytics layer to better serve reliability use cases. We have also advocated a modern trend to acquire tracing data via open source industry standards like OpenTelemetry without vendor lock-in.

Monitoring Redis, the open source in-memory data platform, is complicated enough when you are hosting your Redis instance on just a single server. It gets even more complex when you build a Redis cluster that consists of multiple nodes and distribute your data across them. But as long as you know which metrics to prioritize and how to collect them, Redis monitoring is feasible enough. This article offers an overview of how to monitor the state of Redis database clusters.

At Sumo Logic, our belief is that security operations is no longer a human scale problem. We need tools and technologies to aid our defenders and responders to be able to process, investigate and respond at machine speed. Our vision for modernizing security operations to deal with threats at machine scale has always encompassed more than just SIEM.

We are seeing a renewed focus on accelerating digital transformation projects across business ecosystems and workflows within our customer base. These projects are enabling key business outcomes and this organizational transformation has given security and IT leaders the catalyst and opportunity to modernize security operations while eliminating on-premises debt.

In a recent press release entitled ‘Sumo Logic Achieves FedRAMP Moderate Authorization’, dated Feb 2, 2021, the pioneer in continuous intelligence announced its Continuous Intelligence Platform™ has achieved Federal Risk and Authorization Management Program (FedRAMP™) Authorization at a Moderate impact level enabling the company to help public sector organizations get real-time insights into their complex on-premises and cloud environments.

One of the more delicate debates in the DevOps world is what observability has to do with monitoring. Is observability just a trendy buzzword that means the same thing as monitoring? Is observability an improved version of monitoring? Are monitoring and observability different types of processes that solve different problems? The answer to those questions depends in part on your perspective.

With almost every blog you read about monitoring, troubleshooting, or more recently, the observability of modern application stacks, you’ve probably read a statement saying that complexity is growing as a demand for more elasticity increases which makes management of these applications increasingly difficult. This blog will be no exception, but there’s a good reason for that: we just enabled the first Sumo Logic customers with powerful new tools to tackle these exact challenges.

It’s been almost a year since I shared some thoughts about distributed tracing adoption strategies on this blog. We have discussed how different approaches between log vendors and application performance management (APM) vendors exist in the market and how important that is to allow users to analyze the data, including custom telemetry, the way they want.

Java Management Extensions, or JMX, was first added to J2EE, and it has been part of J2SE since the 5.0 release. The JMX API aims to provide a standard for monitoring and managing Java-enabled applications and services. In this article, we will explain the JMX architecture and show you how to pull the metrics that it generates into your Sumo Logic account in order to gain unique insights and a more thorough understanding of the health of your application and services.