While owners of Samsung devices continue to drive the majority share of North American Android Web traffic, LG posted the largest usage share gains among any Android brand between June and September 2014. Additionally, North American Android Web traffic continues to be heavily driven by smartphones as opposed to tablets, with the current 81% to 19% split widening by 2 percentage points since our previous study.
iPhone 6 users now generate 2.3% of total North American iPhone-based Web traffic - a figure roughly 0.8 percentage points higher than what was observed the first weekend following release. Meanwhile, the share of U.S. and Canadian iPhone-based Web traffic driven by iPhone 6 Plus users reached 0.3%, an increase of 0.1 percentage points since the first post-release weekend.
As an organization, Chitika has wholeheartedly invested in building out the Chitika Insights research program through staffing, rigorous process management, and a quality data infrastructure. Additionally, our reports rely on our vast trove of ad impression data and our knowledgeable and skilled team of data scientists. It’s this impression-level data and attention to statistical detail that has ultimately led our research to be cited by The New York Times, Wall Street Journal, CNN, Bloomberg, and many others.
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.