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Serverless cloud infrastructure is the next step in building apps. But if you’ve tried to navigate the vast number of services in the AWS console, you know that using the cloud is easier said than done. Today’s developers are overwhelmed with the amount of services AWS offers.
The solution? AWS Amplify, which helps developers to easily build and deploy complete mobile and web apps by providing a collection of CLI tools, libraries, frameworks, and cloud services.
Historically, in monolithic architectures, logs were stored directly on bare metal or virtual machines. They never left the machine disk and the operations team would check each one for logs as needed.
This worked on long-lived machines, but machines in the cloud are ephemeral. As more companies run their services on containers and orchestrate deployments with Kubernetes, logs can no longer be stored on machines, and implementing a log management strategy is of the utmost importance.
As the number of services grows in an organization, the problem of secret management only gets worse. Between Zero Trust and the emergence of microservices, handling secrets such as tokens, credentials, and keys has become an increasingly challenging task. That’s where a solution like HashiCorp’s Vault can help organizations solve their secret management woes.
Although there are secret management tools native to each cloud provider, using these solutions locks you in with a specific cloud provider. Vault, on the other hand, is open source and portable.
With cloud adoption on the rise, the level of abstraction in application architecture has increased — from traditional on-premises servers to containers and serverless deployments. The focus on automation has also increased to the point where manual intervention is no longer preferred, even for infrastructure-related activities like backups, security management, and patch updates. This desired state equates to a NoOps environment, which involves smaller teams that can manage your application lifecycle. Ideally, in such an environment, the efforts required by your operations team will be eliminated.
It is beyond debate that DevOps is now deeply integrated into the DNA of all cloud-first organizations and is today more of a norm than a rarity. Cloud applications demand agility, and DevOps delivers it. However, does NoOps mean the end of the DevOps era? Or is it simply the next step in the progression of DevOps?
Having well-defined guidelines in place can facilitate your software projects, especially more complex ones. But, you don’t necessarily need to invest the time and effort to document these practices from scratch. Instead, teams can leverage best practices documented by other reputable companies and adapt them for their own projects.
The Twelve-Factor App methodology has been around for a decade now. But, how relevant and important are these guidelines today? Here, we examine just how well they’ve held up in addressing architectural, deployment, and operational concerns in building software at scale.
This article was originally published at Coder Society, and we’ll explore GraphQL’s core features, how to interact with a GraphQL API, and some development and operational challenges.
Nowadays, REST seems to be the default approach for building APIs, typically based on the familiar HTTP protocol. While REST is relatively simple to work with and enjoys widespread popularity, its use of multiple endpoints to address resources sometimes gets in the way of flexibility.
What is the best way to build an API today? REST probably comes to mind, but if you’re going to make the investment to build new software, it’s probably worth considering a few different options and choosing the best among them.
GraphQL stands out as an alternative to the REST API architecture mainly (but not only) because it provides a discoverable API by design. It also comes with its own query language and a runtime for fulfilling queries via functions called resolvers.
Loki is an open-source, multi-tenant log aggregation system. It can be used with Grafana and Promtrail to collect and access logs, similar to the ELK/EFK stack. While one can use Kibana and Elasticsearch to make advanced data analysis and visualizations, the Loki-based logging stack focuses on being light-weight and easy to operate.
Loki provides a query language called LogQL, which allows users to query logs. It is inspired by Prometheus’ PromQL and can be considered to be a distributed “grep” that aggregates log sources.
This article was originally published at Coder Society.
The last decade has seen a huge rise in cloud computing.