Operations | Monitoring | ITSM | DevOps | Cloud

Introducing Log Observability for Microservices

Two popular deployment architectures exist in software: the out-of-favor monolithic architecture and the newly popular microservices architecture. Monolithic architectures were quite popular in the past, with almost all companies adopting them. As time went on, the drawbacks of these systems drove companies to rework entire systems to use microservices instead.

6 Bugsnag Alternatives to Consider in 2021

Modern customers demand that their applications are as seamless and error-free as possible. However, building such apps is a herculean task in itself. You need to constantly look out for incoming exceptions and warnings in your app in production. Effective error monitoring is key to resolving such issues before they are discovered by your users and cause a disruption in the quality of your services.

REST vs CRUD

CRUD and REST are two of the most popular concepts in the Application Program Interface (API) industry. REST was made to standardize the HTTP protocol interface between clients and servers and is one of the widely used design styles for web API. On the other hand, CRUD is an acronym used to refer to the four basic operations executed on database applications. Because both work on manipulating databases’ data, it’s easy to see why people have some confusion between them.

Open Source for Better Observability

Monitoring cloud-native systems is hard. You’ve got highly distributed apps spanning tens and hundreds of nodes, services and instances. You’ve got additional layers and dimensions—not just bare metal and OS, but also node, pod, namespace, deployment version, Kubernetes’ control plane and more. To make things more interesting, any typical system these days uses many third-party frameworks, whether open source or cloud services.

Perfect Your Cloud's Deployment with Logz.io & AWS CloudFormation Public Registry

AWS CloudFormation is a service that enables you to create and provision AWS infrastructure deployments predictably and repeatedly. This helps you leverage AWS products such as EC2 instances, Amazon Elastic Block Store, Amazon SNS, Elastic Load Balancing, and Auto Scaling to build highly reliable, highly scalable, cost-effective applications in the cloud – without worrying about creating and configuring the underlying AWS infrastructure.

Introducing new integrations to make it easier to monitor Vault with Grafana

HashiCorp Vault is an increasingly popular multi-cloud security tool that allows users to authenticate and access different clouds, systems, and endpoints, and centrally store, access, and deploy secrets. At Grafana Labs, we’re always looking for ways to make it easy for our community to get started monitoring important parts of their systems. So we’re happy to share some new integrations that will help our users get the most out of Grafana + Vault.

8 Months of OnlineOrNot: From 7 Day MVP to Stable Product

September and October were relatively quiet, so I thought I would write a single article for both months. While I'd normally try to write at least one useful article per month for OnlineOrNot's audience (as well as an update on how the business is going), I wrote no articles, and no code, actually. Instead, I packed up my life in Sydney, Australia, escaped lockdown, and relocated to France with my wife, and just enjoyed living for a while.

Change Happens - Get Alerted

To give you enough notice to fix an issue before it escalates, we’re evolving our alerts and making them more proactive with Change and Crash Rate Alerts. So when your application experiences a change from the norm or a dip in crash-free sessions, Sentry will (smartly) alert you via Slack, Teams, PagerDuty, or old-fashioned email.

What can you learn from IoT with i2M - Part 2

In the first part, I outlined some of the terms associated with the delivery of IoT. Next, let’s look at how this gets complex. You will need to read the state of each sensor (through their appropriate API and through their appropriate vendor-supplied hub), create logic to determine what actions must be taken when certain conditions are met, and then deliver these as a workflow to each responder, and confirm through data collected from sensors that the requested change was implemented.