In May 2019, Bloomberg Government reported that Federal agencies planned to move 272 information technology programs to the cloud in FY2020. Fast forward to April 2020 — they reported that there are more than 1,800 federal IT programs that are either migrating or considering migrating to the cloud in fiscal 2021, signifying a rapid increase in cloud adoption in the federal government. How might COVID-19 affect this explosive increase in cloud interest?

Editor’s note: This is a follow up to a recent post on how to use Cloud Logging with containerized applications running in Google Kubernetes Engine. In this post, we’ll focus on how DevOps teams can use Cloud Monitoring and Logging to find issues quickly. Running containerized apps on Google Kubernetes Engine (GKE) is a way for a DevOps team to focus on developing apps, rather than on the operational tasks required to run a secure, scalable and highly available Kubernetes cluster.

DevOps is an approach to software development and delivery that emphasizes collaboration between different stakeholders. DevOps also places priority on automation and the integration of different tools in a single, well-oiled pipeline. Ultimately, DevOps boils down to cultural values and goals. When it comes to tooling and processes, there is no one right way to “do” DevOps; a variety of different approaches are possible.

The large volumes of logs, metrics, and traces generated by scaling cloud environments can be overwhelming, but they must be collected to identify and respond to production issues or other signals showing business or application issues. To collect, monitor, and analyze this data, many teams choose between open source or proprietary observability solutions.

This post is the first in a three-part series on how to effectively monitor the hosts and systems in your ecosystem, and we're starting with the one you use most: your personal computer. Metrics are a key part of observability, providing insight into the usage of your systems, allowing you to optimize for efficiency and plan for growth. Let's take a look at the different metrics you should be monitoring.

NoSQL technology has become more popular in recent years thanks to the development of new open-source NoSQL databases that are relatively easy to install, use and integrate with web frameworks. An example of one of those popular frameworks on the internet is known as MEAN (MongoDb, Express.js, Angular.js, Node.js). These NoSQL frameworks have become very popular for things such as content management, catalogs and big data in general.

From dealing with security concerns to production monitoring, businesses need to analyze the log data of their systems to ensure everything is functioning normally. In a computing context, a log refers to automatically produced and time-stamped documentation of events related to a particular system. Analysis of log data helps businesses comply with regulations, security policies and audits, understand online consumer behavior, and comprehend system troubleshoots.

At Splunk, we're listening to our customers and offering more predictable, flexible, and familiar pricing options as part of our Data-to-Everything Pricing model. In particular, Splunk’s new infrastructure pricing metric changes the paradigm of how much data you can analyze with Splunk, allowing users to move toward a value-driven pricing model that better aligns what you pay with real value you can extract from using Splunk products.

When applications experience performance issues, developers may rely on heuristics and find the root cause of issues by directly looking into their code. However, in modern application environments, several services, third-party APIs, and cloud-based components can make it difficult to pinpoint the root cause of an issue. Application logs can help developers detect and resolve issues faster.

GrafanaCONline is live! We hope you’re able to check out all of our great online sessions. If you aren’t up-to-date on the presentations, here’s what you missed on day 7 of the conference.