SREcon16 is a wrap, and our team had a blast at this year’s event! Both days were non-stop action: demos, discussions, and - of course - handing out our fair share of panda swag. Between the buzz on the floor and in the sessions, what topics were top of mind at this year’s show? Here are our three key takeaways:
As a TechOps community, we’re awash in buzz words. Most are initially used to establish geek credibility yet quickly become cliches. Take, for example, the term “DevOps”. From its inception as a Twitter hashtag used to promote a meetup in bucolic Ghent, Belgium in 2009, it began to be co-opted months later by ops teams around the world aspiring to manage infrastructure with code.
This is part two of a two-part post about using event correlation to thwart DDoS attacks. Channeling Mark Twain: it would have been shorter if I had more time. In the last post I described why DDoS attacks for SaaS providers are no different than performance and availability issues experienced in other domains like healthcare, finance, or retail. In this post I’ll share a customer story about a security breach that never happened… thanks to a savvy DevOps team and data science.
Every company’s a target, every customer’s at risk. But the now-cliched threat of data breaches from Distributed Denial of Service (DDoS) attacks obscures a bigger threat: outages that impact not just data integrity but also profitability, brand equity, and customer retention.
The volume of attacks is growing and so is the impact of down time. According to Akamai’s most recent State of the Internet report, DDoS attacks are a bigger threat than ever before. “The number of DDoS attacks continued to increase substantially in Q2 2015, more than doubling the number observed in Q2 2014.”
In the last two decades, with the emergence of cloud infrastructure and SaaS delivery models, the monitoring ecosystem has changed dramatically to include over 100 monitoring solutions. The upside of that change is the rapid implementation of monitoring infrastructure, but the unintended consequence of this is that the tools themselves decide what IT measures.
At BigPanda, we always enjoy hearing about our customers’ monitoring setups. A fascinating pattern we’ve noticed is the uniqueness of each setup.
Data center growth over the last 15 years has created significant growing pains in terms of data center management. Tasks that once could be done manually by IT teams have hit the limits of scalability, cost, and efficiency. The key to enabling IT to meet these challenges involves one key theme: automation.
Anomaly detection for monitoring has been a trending topic in recent years. And while the math behind it is fascinating, too much of the discussion has revolved around histograms, moving averages and standard deviations. More discussion needs to happen around its practical applications, and for that reason, this practical guide to anomaly detection will attempt to provide an actionable overview of current off-the-shelf anomaly detection tools.