re:Invent 2015 was, in a word, “big”. It was a big event, with over 18,000 people representing about a 50% increase over last year. There was a lot of big news and the requisite parading from a lot of big companies (Time, John Deere, Intel, and BMW to name a few). The net result being big money for Amazon Web Services. Not only is AWS profitable, but it’s experiencing year over year revenue growth eclipsing 80%, with Q2 alone sitting at $7.3B.But the really “big” takeaway from the event was the heavy focus on Big Data. This is a remarkable and rapid transition from just a year ago when “all-in” transition stories and web presence migration were all the rage. Particularly noteworthy was a higher education panel discussion that wasn’t intended to center on Big Data, but seemed to be all the panelists wanted to discuss. Big Data was the recurring theme in nearly every session and the centerpiece of product announcements from Amazon. Specifically: ElasticSearch Managed Service: Technically this announcement preceded Re:Invent by a few days, but was nonetheless an acknowledgement of the incredible popularity of the NoSQL data store. Not only is AWS making ElasticSearch available as a service, but they rolled it into their existing Identity and Access Management module, which will make supporting secure workloads considerably easier. QuickSight: Easily the biggest announcement at the event, AWS continues to play frenemy to SaaS providers running on AWS. No longer contented to play in the IaaS and PaaS space, QuickSight represents a legitimate foray into the SaaS market and a huge bet on Big Data following the success of their RedShift and Dynamo DB offerings. Snowball: Is the “never underestimate the throughput of a truck full of drives” ethos turned product. Clearly aimed at enterprises running Big Data workloads on their existing infrastructure, this 50TB box overcomes a major obstacle to massive workload migration. Left unsaid, but equally amazing is Amazon’s ability to create a solution that leverages their expertise in logistics, products (eInk), and web services. For a company of it’s size and structure, this kind of broad cooperation speaks volumes of their ability to drive cross-divisional cooperation, which is no easy feat. These breadth increasing offerings were in addition to a number of depth focused Big Data items that AWS rolled out to ease migration and improve existing product offerings. These included the Kinesis Firehose, database migration and schema transition tools, the new X1 instance type (2TB of RAM) as well as (wait for it) Python support for Lambda (much to the chagrin of Dr. Vogels)! From the applause received by the latter announcement it’s clear that AWS has largely achieved the stated goal of keeping Re:Invent technical. If it weren’t already, it’s now obvious Big Data is going to be a big deal for the foreseeable future. While AWS likes to place a heavy emphasis on their largest customers when it comes to case studies, it’s really the mid to small market where the cloud transition and Big Data enablement becomes a bigger story. Companies like GE, John Deere, and Time have been in the data business for years. For them the cloud represents improved efficiency, scalability, and reductions in time to market – but it doesn’t fundamentally transform their business model. For small to mid-size companies that have historically lacked access to Big Data on account of very high startup costs, the cloud really is a game changer. With offerings like Redshift, Elastic Map Reduce, and Kinesis already pushing infrastructure setup costs down, AWS is taking a swing at lowering ramp-up, training, and licensing hurdles through offerings like QuickSight and managed ElasticSearch. These product offerings not only drive adoption by lowering barriers to entry but also drive retention as they are platform-dependent. Nobody would fault Amazon for taking a loss-leader strategy on AWS to drive adoption and retention, but the fact that they don’t have to is a testament to just how far ahead of everyone they are. And speaking of everyone else, Amazon’s foray into the SaaS market with QuickSight should not be overlooked. While they were very intentional in offering API access to QuickSight for existing BI tools (Tableu, Splunk, etc.), the ramifications of the decision are pretty clear. Amazon will not be contented to remain in the IaaS and PaaS space. A great decision given the likely commoditization of these markets over time. So one has to wonder – what (or who) does Amazon choose to go after next? Sitting through the keynote addresses, technical sessions, and in meetings one couldn’t help but wonder how much longer AWS will be able to completely dominate this market. While it’s clear their main competitors’ unwillingness to cannibalize their existing business models has cost them dearly, I’m not yet convinced that AWS can continue to compete with such breadth and depth without simultaneously exposing some opportunities to their competitors. So, where does all of this leave us? For starters, the investments AWS is making in building out and supporting their partner network are a great decision as it allows their partners to go deeper and wider with the platform. The show floor was full of capable partners, such as Novetta, who add considerable value to an ever improving platform with a growing customer base. For companies that specialize in Big Data and customers that want to take advantage of it, AWS really is a game changer – and now everyone knows it!
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