Virtana recently published the results of a new State of Hybrid Cloud survey. One of the findings is that 81% of companies in the study who have started their migration to the public cloud have engaged multiple providers. This result tallies with a recent Gartner survey of public cloud users, in which 81% of those respondents said they are working with two or more providers.
Python is one of the best programming resources available for designing machine learning systems. With a variety of technical abilities and potentially time-saving loops and processes, it can be an invaluable tool. However, it’s these capabilities that also make Python difficult to use. In many cases, Python may seem sluggish as it tries to navigate intricate, complicated strings of code.
Fair warning, I tried to avoid using the obligatory good news, bad news opener for this article, but I couldn’t help myself. It’s just that we sort of are living in the age of good news, bad news. Especially when it comes to IT support and remote work. So here it goes. First, the good news: If you work in IT support, your colleagues need your expertise now more than ever before.
Are you interested in performing time series forecasting or anomaly detection, but you don’t know where to start? If so, you’re not alone. There is an overwhelming variety of libraries, algorithms, and workflow recommendations for these tasks. As a Developer Advocate at InfluxDB, the leading time series database, I’ve researched time series data science methodologies and best practices for forecasting and anomaly detection.
If you've ever had to be on the receiving end of a monitoring system that uses email for alerts, you know how noisy things can get. Particularly if you're working in an agency or freelance-like environment, with dozens of client sites to maintain. You get so many emails that you start looking into integrations with third-party services like Zapier, and coming up with more and more complex rules to try reduce the noise.
During the next nine weeks, our team will work to improve the overall experience of Qovery. We gathered all your feedback (thank you to our wonderful community 🙏), and we decided to make significant changes to make Qovery a better place to deploy and manage your apps. This series will reveal all the changes and features you will get in the next major release of Qovery. Let's go!
When DevOps teams talk about monitoring a database, the primary motivation is to ensure that the database won’t suffer a performance hiccup. Long queries, timeouts and table scans are among the most popular causes behind lousy customer experience. However, in recent years, more data has been shifted to cloud databases.
In an expanded and commercial industry space new and innovative opportunities are presented to retail organizations from several novel sources of data, log files, and from transaction information to sensor data to achieve unmatched value and combative advantage in their respective fields. In order to attain customer satisfaction and to flourish their business, people across the organization must be empowered, so that swift and accurate decisions can be attained.
Five worthy reads is a regular column on five noteworthy items we discovered while researching trending and timeless topics. In this week’s edition, let’s explore how quantum computing works and how it impacts cybersecurity.
Troubleshooting issues in your applications can be a complicated task requiring visibility into various components. In the worst-case scenario, to understand what is happening and why it is happening you will need metrics, logs, and traces combined together. Having that information will give you the possibility to slice and dice the data and get to the root cause efficiently. In this article, we will focus on logs and how to configure logging for your Java applications.
