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.
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.
PagerDuty is excited to introduce Round Robin Scheduling. Round Robin Scheduling allows teams to equitably distribute on-call shift responsibilities amongst team members. Automatically assigning new incidents across different users or on-call schedules on an escalation level ensures that teams are resolving incidents as efficiently as possible. And, by balancing the workload across multiple users, there’s less risk of burnout.
In the past, we’ve written about what instrumentation is and the insights it provides. Instrumenting your code generates telemetry that shows you how your system is performing, and whether your system is healthy. Like with most other companies, at Honeycomb we don’t write all of the code that runs in our systems.
As we kick off a new year, so does new and refreshed thinking in the public sector. With another full year of lessons learned from the pandemic, public sector leaders will apply these insights in the way they execute their documented data strategies. Synthesizing what we see and hear from our stakeholders, below are five of the needle movers we see in public sector data strategy execution.
