Apple began pushing out iOS 8 updates to eligible devices around 1pm ET on September 17, 2014. Unlike with iOS 7, which boasted a wide variety of differences from its predecessor iOS 6, in particular a brand new look and feel, iOS 8 was more incremental in its improvements. These include the ability to add widgets, extensions to share information between apps, and interactive notifications. While reviews of the new OS version have been generally positive, initial adoption of iOS 8 has been remarkably more tepid than the last two iOS iterations – iOS 7 and iOS 6.
In our last edition of Logging Data, we introduced Cluster Map Reduce, or CMR. The new tool acts as an alternative to Hadoop and HDFS when paired with a POSIX compliant clustered file system, simplifying the movement of data through the analytical back end, and helping to minimize the dependencies and potential points where the data pull process may slow or stop altogether. Today, we’re proud to provide this tool to the world as a free, Open Source release!
On September 9, 2014, Apple is expected to announce one or more new iPhone models, along with some news regarding the availability of the newest editions of iOS and OS X. Going into the event, usage-based stats show that Apple has done a noteworthy job managing its own mobile ecosystem from an adoption standpoint.
Similar to the North American ecosystem, Apple iPad users generate the lion’s share of tablet-based Web traffic within the UK. Additionally, Samsung, Amazon, and Google users represent the next-largest UK tablet traffic segments, albeit in a slightly different order than what is observed in the U.S. and Canada.
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.