Thus far, our Logging Data series has focused on the nuts and bolts of our network operations and data infrastructure. While we employ some terrific software and hardware, our proverbial secret sauce consists of the various customizations we employ using these tools. No place was this more evident than during the transition from HDFS to Gluster, and the subsequent porting of Hadoop resources. The team here is well versed in working around issues, so after some brainstorming, the solution pretty much morphed into “Let’s just build something internally that fulfills our needs better than Hadoop.” Not an easy task, but one that our Operations and DI teams took on readily
Over two and a half years following the release of its Kindle Fire tablet, Amazon released its first smartphone on July 25, 2014. While the Fire Phone was listed atop Amazon’s Best Seller list for several days in early August, North American usage of the device has grown only incrementally, rather than exponentially, in the three weeks following the smartphone’s launch as an AT&T exclusive.
We’ve briefly mentioned our implementation of Infiniband in both of the previous Logging Data posts without giving a thorough explanation of its function and capabilities within our architecture. In this latest installment, we’ll be doing just that, along with discussing our corresponding Hadoop framework.
An earlier UK-based smartphone usage study found that Apple, Samsung, and BlackBerry users generated more than 86% of the country’s total smartphone Web traffic in June 2014. The latest Web traffic statistics for North America demonstrate a more diversified market on a brand basis, but Apple and Samsung users remain the biggest smartphone traffic drivers by a sizable margin.
Developer interest in OS X Yosemite had already outpaced its predecessor, OS X Mavericks, one month following its unveiling at WWDC 2014. Apple subsequently released a public beta of the new OS on July 24, 2014, and North American Web traffic data show associated usage rates rising significantly.
The previous installment of our Logging Data series outlined how individual impressions move through our network. In this edition, we’ll discuss the necessary storage considerations cataloguing all of these impressions effectively 24 hours a day, specifically focusing on the challenges that result from the requirements of ad network operations.
Since April 2014, the share of tablet Web traffic generated by North American Apple iPad and Kindle Fire users has increased by 0.8 and 1.2 percentage points, respectively. These represent the two largest quarter-over-quarter increases for any tablet brand, while Samsung’s user base exhibited the largest share loss over the same timeframe, dropping two full percentage points.
In a memo to employees sent on July 17, new Microsoft CEO Satya Nadella suggested the company will take a focused, but long-term approach to gaining traction in the high-end smartphone market with its Nokia brand. While any potential geographic shifts to this strategic model are unclear, within North America, a plurality of Windows phone Web traffic is driven by users of Nokia’s more entry-level Lumia models, as opposed to its flagship devices.
In this “Logging Data” series, we’ll provide some in-depth detail on the intricacies of data collection, infrastructure, and access here at Chitika, hopefully providing some useful lessons for both newcomers and veterans in the field. Our first post will focus on our logs – the baseline of our data collection – and the subsequent processes that coalesce the information they contain into more readily accessible formats for our data scientists.
Similar to what we’ve observed in the North American marketplace, UK-based Apple users generate the greatest share of that nation’s smartphone Web traffic. However, a current and historical brand-based distribution of remaining smartphone Internet usage highlights a number of differences from the U.S. and Canadian ecosystem, and some unique aspects of the UK smartphone user base.