Operations | Monitoring | ITSM | DevOps | Cloud

Maintaining an endpoint, especially a customer-facing one, requires constant monitoring, whether using REST or GraphQL. As the industry has looked for solutions to build a more adaptive endpoint technology, it is also a must to monitor these endpoints. GraphQL and REST are two different technologies that allow user-facing clients to link to databases and platform logic. Both GraphQL and REST include monitoring techniques.

In our increasingly hyper-connected, data-dependent world, it can be difficult to keep track of where resources are, how to access them, and how to put data assets to work to run a more efficient and reliable enterprise. Traditional approaches to IT operations analytics are becoming outmoded as the sources and types of data grow more mobile, ephemeral, diverse and distributed.

Cloud technology has enjoyed exponential growth over the past several years. Increases in broadband and wireless speeds have spurred a rise in everything from cloud storage to Software-as-a-Service (SaaS). Before the cloud grew into its current form, it was primarily a tool for backing up data in a safe, remote location. Throughout the evolution of cloud, backup and restore remains one of this technology’s most widely-used and important functions.

If you’re a person who works from home, you almost certainly have to deal with occasional internet connection issues. More often than complete outages, you’re likely dealing with occasional slowness. And you know from experience that any one of dozens of devices and services along the path can cause latency.

Like any great technology, the interest in and adoption of Kubernetes (an excellent way to orchestrate your workloads, by the way) took off as cloud native and containerization grew in popularity. With that came a lot of confusion. Everyone was using Kubernetes to move their workloads, but as they went through their journey to deployment, they weren’t thinking about security until they got to production.

Data is the lifeblood of businesses, and protecting it is incredibly important. One of the ways DBAs help ensure the continuous availability of data is through database backups, a process involving copying the data and schema from a database and saving them elsewhere for retrieval later. But like any other process in IT, database backups have costs, benefits, and risks associated with them.

Preventing data loss for data in motion is a challenge that LogStream Persistent Queues (PQ) can help prevent when the downstream Destination is unreachable. In this blog post, we’ll talk about how to configure and calculate PQ sizing to avoid disruption while the Destination is unreachable for few minutes or a few hours. The example follows a real-world architecture, in which we have.