It’s Friday afternoon, the majority of the development staff has already packed up and headed home for the weekend, but you remain to see the latest hotfix through to production. To your dismay, immediately after deployment, queues start backing up and you begin to get alerts from your monitoring system. Something has been broken and all evidence points to an application performance bottleneck.
A really interesting development I’ve seen in the data and analytic space lately is the rise of the data catalog. You may know these by another name such as a semantic or metadata layer, but they’re all fundamentally the same thing. Data catalogs aren’t new, they’ve been around for a long time. While some vendors like Yellowfin and Cognos have always had them, others like Tableau and Qlik are now just getting to them.
Over the course of my career in Financial Services, I have struggled with how few options I really had when it came to delivering the right information, to the right people, at the right time. It sounds sort of ridiculous considering how much time, money and effort the firms, for which I worked, spent on data warehouses, reporting systems, business intelligence tools and advanced analytics.
You know Big-O is important - not only for acing your next job interview but for knowing how code works at scale. But have you ever taken the time to go beyond a superficial understanding of the subject? In this article, Julie Kent uses equal parts math and Ruby to reveal the beating heart of Big-O and show us how it ticks.
Logging is an essential part of just about any PHP-based application; whether in a script or a larger application. However, how little is too little and how much is too much to log? If we don't log enough information, when something goes wrong, as it invariably does, then we won't have enough information available to determine what went wrong so that we can fix the problem. However, if we have too much information, then we'll be unable to filter out the white noise.
