The huge volume of Big Data produced by sensors, genomic sequencers, electronic exchanges, and connected devices continues to generate headlines but it’s the diverse types of data, not the volume, that’s a bigger challenge to data scientists and is causing them to “leave data on the table.”
Do you require GBs of data to check the performance of your app? The easiest way is to download samples of data from free data repositories available on the Web. But the main disadvantage of this approach is the data will have very less unique content and it may not
Mode is trying to do for data scientists and analysts what GitHub did for developers by giving them a place where they can find, collaborate and work on data. Formation8 led the new round, which also included Reddit’s Alexis Ohanian.
Predictive machine learning models are an important tool for many aspects of e-commerce. At Etsy, we use machine learning as a component in a diverse set of critical tasks. For instance, we use predictive machine learning models to estimate click rates of items so that we can present high quality and relevant items to potential buyers on the site.
Machine data can come in many different formats and quantities. Weather sensors, fitness trackers, and even air-conditioning units produce massive amounts of data, which begs for a big data solution. But how do you decide what data is important, and how do you determine what proportion of that information is valid, worth including in reports, or valuable in detecting alert situations? This article covers some of the challenges and solutions for supporting the consumption of massive machine data sets that use big data technology and Hadoop.
It’s been a busy couple of years here at Microsoft. For the dwindling few of you who are keeping track, at the beginning of 2012 I took a new job, running our “Big Data” platform for Microsoft’s Online Services Division (OSD) – the division that owns the Bing search engine and MSN, as well as our global advertising business. As you might expect, Bing and MSN throw off quite a lot of data – around 70 terabytes a day.(that’s over 25 petabytes a year, to save you the trouble of calculating it yourself). To process, store and analyze this data, we rely on a distributed data infrastructure spread across tens of thousands of servers. It’s a pretty serious undertaking; but at its heart, the work we do is just a very large-scale version of what I’ve been doing for the past thirteen years: web analytics. One of the things that makes my job so interesting, however, is that although many of the data problems we have to solve are familiar – defining events, providing a stable ID, sessionization, enabling analysis of non-additive measures, for example – the scale of our data (and the demands of our internal users) has meant...
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