Operations | Monitoring | ITSM | DevOps | Cloud


How to Implement Circuit Breaker Patterns

Microservices architecture has a lot of moving components, and it is distributed in nature. Distributed applications communicate over networks, which are inherently unreliable. Failures are inevitable when your services communicate with external components like databases, APIs, and remote services. No matter how well you design your cloud native microservices architecture, it is bound to fail at some point—making resiliency a critical feature to keep in mind.

Getting Started with AWS Lambda in .NET

Serverless computing is becoming increasingly popular, and with the proliferation of serverless computing over the past few years, the adoption of Function-as-a-service (FaaS) computing models has also increased. Some of the popular FaaS providers today include AWS Lambda, Microsoft Azure Functions, and Google Cloud Functions. In this article, we’ll examine how you can build and deploy AWS Lambda applications to AWS, as well as how to integrate your Epsagon account for monitoring.

Issue Management and Error Tracking

Modern applications rely on complex ecosystems of multiple tools, processes, and distributed teams. While adopting a DevOps methodology to streamline such workflows is one of the most crucial considerations, an efficient issue tracking mechanism remains a key factor in an application’s overall success. Issue tracking enables a comprehensive view of system errors, malicious user behaviors, and system-level conflicts.

Using Deployment Markers to Enhance Observability

A Deployment is a Kubernetes object that instructs the platform on how to create and modify instances of pods running applications. Deployment objects enable developers to create declarative updates for pods and ReplicaSets, ensuring that at least one instance of the application is always available. To do so, the deployment controller checks cluster nodes and pods for their health status and replaces failed ones.

The Importance of Application and Infrastructure-Level Dashboards in Cloud Environments

In the current infrastructure world, we all understand the critical importance of monitoring when it comes to running a business. Dashboards offer a single pane for a high-level view of all your resources, plus let you drill down to analyze the details. You can even customize various views of your metrics and create alarms. Different services provide their own set of dashboards.

Epsagon's Nitay Milner at AWS re:Invent | "Microservices Observability"

Observability is about more than building a reactionary response to latency and outages. Whether or not you focus on it today, at the core of your team is an “engineering flywheel.” Keeping talented engineers engaged, maintaining a cadence of feature releases, and measuring the impact of new technology can all improve when you tighten the feedback loop on the one thing they all focus on—the service itself. At this session, Nitay Milner will cover the new challenges that microservices architectures have presented and explains how to create an effective observability strategy that can accelerate your engineering flywheel.

Getting Started with AWS Lambda and Ruby

In this article, we’ll guide you through how to develop and deploy a small serverless Ruby application using the Serverless Framework and AWS Lambda. We’ll show you how to create the resources, deploy the code, and troubleshoot scenarios. Most importantly, we’ll highlight the importance of observability by having you integrate your finished app into Epsagon to achieve full monitoring. Sample code for this article is available here

Troubleshooting Using Prometheus Metrics in Epsagon

Observability is crucial for modern application development, as it enables organizations to achieve tighter control over dynamic systems. In addition to the inherent complexities of an application workflow, various cloud-native ecosystems, such as Kubernetes and Prometheus, introduce a number of components within an already distributed framework.

Autoscaling Kinesis Data Streams in Epsagon

Large-scale data processing applications usually consume streams of continuously generated data. Use cases of data streaming can be found in every industry, from monitoring vehicle sensors data to analyzing in-game player interactions. AWS Kinesis data streams is a managed streaming data service that supports the collection and processing of large streams of data in real-time.