If you haven’t considered how risky it is to rely on the Internet to carry your mission-critical customer and employee digital experiences, then the month of June 2019 alone can serve as a great learning opportunity, with three distinct and far-reaching examples of just how fragile the Internet is. On June 2nd, we saw a major Google outage that lasted for four hours, affecting access to various services including YouTube, G Suite and Google Compute Engine.
In the attacker’s world, all vulnerabilities and potential exploits work toward the hacker’s advantage — not yours, not mine. This includes WordPress hacks. While living back east (over a decade ago), I was friends with several small business owners. One weekend morning, the owner of the local photography studio called me at 7 am and said: “I think I’ve been hacked.” I could hear the soft clicking of a keyboard in the background.
OpsRamp delivers real-time observability that IT teams need to understand the performance and availability of business services. Given that modern digital services rely on dynamic and distributed infrastructure, it is critical to pinpoint performance issues that prevent an enterprise from delivering compelling user experiences. So how do you track the end-customer experience as well?
One of the common pieces of advice I hear given to managed service providers (MSPs) is to “go narrow”—find a niche and become a specialist. This is generally sound advice. Specialization typically means your MSP faces less competition and becomes much easier to find in an otherwise crowded marketplace. But finding an area to specialize in is easier said than done. So how do you find a great niche for your MSP?
LogicMonitor is proud to announce anomaly visualization as an addition to our growing AIOps capabilities! With this new functionality, users are able to visualize anomalies that occur for a monitored resource and compare that anomaly to key historical signals, such as the past 24hrs, 7 days, or 30 days. Anomaly visualization complements LogicMonitor’s existing forecasting functionality and provides another layer of intelligence to better understand resource health.
At Datadog, we operate 40+ Kafka and ZooKeeper clusters that process trillions of datapoints across multiple infrastructure platforms, data centers, and regions every day. Over the course of operating and scaling these clusters to support increasingly diverse and demanding workloads, we’ve learned a lot about Kafka—and what happens when its default behavior doesn’t align with expectations.
In my last two posts, I wrote about how to send mass emails with AWS Lambda and the lessons I learned along the way. This post is all about how to take that serverless design to the next level by maximizing my observability into every function running. In this design, three main functions were responsible for getting the emails out the door.