If you’ve ever experienced downtime during sudden traffic spikes, then have no fear: Auto-scaling is here. That’s right. As of today, all Dedicated plans on Platform.sh that have subscribed to the Observability Suite package now benefit from auto-scaling out-of-the-box.

Depending on your business reason for migrating to the cloud, you can either move in one go or incrementally. If, however, you prefer to retain some of your operations, app design, or workflows on-premise, you can still do so based on the cloud migration strategies you use. Sometimes, it makes more sense to modify your existing system rather than make too many changes all at once. Cost, data security, and service availability (thus revenue and customer experiences being impacted) are just three concerns.

The new version of the Google logging client library for Go has been released. Version 1.5 adds new features and bug fixes including new structured logging capabilities that complete last year's effort to enrich structured logging support in Google logging client libraries. Here are few of the new features in v1.5: Let's look into each closer.

Three great things that do not always work great together. In the beginning there were large computer systems that few organizations could afford. Over time these systems became smaller and cheaper and many (if not most) organizations took advantage of them. Some just at the end-user level (i.e. the IBM PC on the desk), some only at the high-end level (i.e. a mainframe in the data center with terminals on desks), and some in a combination of both (anyone remember Reflection?).

The utilization of spot instances can be one of the fastest and easiest ways to drastically reduce the compute costs of your Spark infrastructure when you run in the cloud. In this article, we will discuss two techniques specific to Spark on Kubernetes, Enhanced Spot Instance Selection and Executor Decommissioning, that can both remove some pitfalls of spot instances and increase the reliability of your compute – resulting in faster, cheaper applications.

Cloud-native applications running on Kubernetes rely on container network plugins to establish workload communication. While Azure Kubernetes Service (AKS) provides several supported networking options (kubenet and Azure CNI) that address the needs of most deployments, Microsoft recently introduced the ability to bring your own networking solution, called BYOCNI, to help users address more advanced networking requirements. This new feature enables AKS customers to run Calico networking on AKS.

According to a recent CNCF survey, 86% of the cloud native community reports that they use Prometheus for observability. As Prometheus becomes more of a standard, an increasing number of developers are becoming fluent in PromQL, Prometheus’ built-in query language. While it is a powerful, flexible, and expressive query language, PromQL is typically only able to query Prometheus time series data.

Your company is growing, and so is your data. That’s a very good thing! But you don’t want to become completely overwhelmed with all your positive growth. As your business expands, thrives, and grows, you’re quickly going to find that you need one centralized and secure location to keep all of your company's files and important documents. Sure you have a large hard drive that supports all you need now, but will it hold up to future demands and all your data is really safe inside it.

Cribl.Cloud’s Summer 2022 release is now available in an AWS cloud near you! As part of this release, we are excited to share the features we have been building, including the latest Cribl product releases (Stream 3.5 and Edge 3.5). This release brings some much-requested features that will help customers increase their compliance, reduce overall costs, and deploy a more resilient observability data pipeline.

Depending on your available resources, data analysis can take place at the edge or in the cloud, but businesses don’t need to choose one location over the other. There are benefits to giving the edge autonomy to collect, process, and act on data locally. Data replication helps maintain edge autonomy and makes it easier for users to get the data they need, where they need it.