If you’ve ever used Ruby on Rails, you’ve probably come across the concept of concerns. Whenever you jumpstart a new Rails project, you get a directory app/controllers/concerns and app/models/concerns. But what are concerns? And why do people from the Rails community sometimes talk badly about them?
Today, Snowflake began life as a publicly traded company on the New York Stock Exchange. What does it mean? It depends on who you are. For employees, this is of course a huge milestone, especially for the longest serving employees who hired on at the company in 2013 when the company first started staffing beyond its core founding team.
Neural Guard produces automated threat detection solutions powered by AI for the security screening market. With the expansion of global trends like urbanization, aviation, mass transportation, and global trade, the associated security and commercial challenges have become ever more crucial.
Refine customer success data modeling with more detailed ticket tracking
One of the Scout’s key features is its ability to quickly highlight N+1 queries in your application that you might not have been aware of, and then show you the exact line of code that you need to look at in order to fix it. In this video, we will use a Ruby on Rails application as an example, but the same concepts apply to other popular web frameworks.
It's easy to get lost in today's continuously changing landscape of cloud native technologies. The learning curve from a beginner's perspective is quite steep, and without proper context it becomes increasingly difficult to sift through all the buzzwords. If you have been developing software, chances are you may have heard of Kubernetes by now. Before we jump into what Kubernetes is, it's essential to familiarize ourselves with containerization and how it came about.
Over the last decade, data collection has become a commodity. Consequently, there has been a tremendous deluge of data in every area of industry. This trend is captured by recent research, which points to growing volume of raw data and growth of market segments fueled by that data growth.
A Forbes survey shows that data scientists spend 19% of their time collecting data sets and 60% of their time cleaning and organizing data. All told, data scientists spend around 80% of their time on preparing and managing data for analysis. One of the greatest obstacles that make it so difficult to bring data science initiatives to life is the lack of robust data management tools.
