Google has announced a rebranding and restructuration of select products from its portfolio under the rubric “Google Cloud”. Google Cloud encompasses the Google Cloud Platform, Google’s productivity apps such as Gmail and Google Docs as well as its suite of machine learning tools and mapping applications. All Android devices and Chromebooks are now part of the Google Cloud as well. Importantly, the Google Cloud brand also features services that propel the digital transformation of enterprises by providing consultative and engineering support and professional services to help organizations transform their digital persona in collaboration with Google’s suite of cloud and machine learning products. The restructuration of a multitude of Google’s products under the Google Cloud umbrella, in conjunction with its foregrounding of service offerings geared toward digital transformation, marks a significant first step with respect to Google’s rebranding as a cloud product and service geared toward the digital needs of the contemporary enterprise. With the unveiling of the new Google Cloud brand, Google squarely positions itself as a partner of companies actively engaged in digital transformation and extends the tentacles of its products and services deeper into the enterprise.
On September 8, Google announced its intent to acquire Apigee, a company that specializes in application programming interface (API) management. APIs are used to connect data from different applications, systems and databases to one another, thereby enhancing the ability of applications to share data and collaborate. Walgreens, for example, uses Apigee to empower developers to build apps using Walgreen APIs marked by the ability to use mobile apps to order photographs at the store of their choosing or submit prescription refills. Google’s acquisition of Apigee expands its ability to integrate APIs into the Google Cloud Platform, thereby enabling the Google Cloud Platform to integrate with a greater variety of applications and infrastructures. For starters, Google’s acquisition of Apigee positions the Google Cloud Platform to seamlessly integrate with Apigee’s impressive roster of customers including AT&T, Burberry and First Data, in addition to Walgreens. The acquisition of Apigee underscores Google’s commitment to building a developer-friendly cloud computing platform that empowers developers to create APIs either for new apps or to enrich existing the capability of existing apps. By giving developers freedom with respect to their choice of a development framework, in addition to an API marked by a battle-tested degree of security and stability, Apigee stands to augment the ease with which Google Cloud Platform and its constituent portfolio of products can integrate with the broader app development and infrastructure ecosystem. In a blog post, Diane Greene, the head of Google’s Cloud Business, noted that Google Cloud Platform plans to deepen its integration with Kubernetes in a further indication that Google plans to augment its efforts to court developers by enriching their experience with, and range of options associated with the Google Cloud Platform. Google will acquire Apigee for $625M or $17.40/share.
Editorial note: This article was authored by Scott Jeschonek, Director of Cloud Solutions, Avere Systems. The opinions expressed below are those of the author, Scott Jeschonek.
Chinese military strategist Sun Tzu once wrote that battles are won or lost before they are ever fought, but can the same be said for the cloud wars? Though many industry thought leaders have made projections, the future of how the battle between public cloud providers will unfold remains hazy. Despite projections that global IT spending will fall in 2016, investment in public cloud services is expected to grow 16% this year, fueling the fire of the on-going war. Most industry experts would agree that AWS, Google Cloud Platform, Microsoft Azure and IBM Cloud Services are the key players to watch, however many CIOs still struggle with determining which cloud service provider is right for them.
The table stakes for becoming the top public cloud provider are only getting higher with each passing quarter, and at the same time, the rise of a multi-cloud strategy is shaking things up. So, how will the cloud wars play out and how can enterprises that want to embrace the cloud choose the right CSP (or combination of CSPs) for their organizations?
When the technical features of the four hottest CSPs put them on relatively even playing field, it is the brand personalities and customer experience that can help organizations evaluate which cloud provider is most suitable for their needs. Below is a breakdown of how each CSP’s culture and decades-long experience with their respective specialties has influenced their approach to building and selling cloud offerings.
Launched officially in 2006, AWS is the oldest among the major CSPs and draws strongly from its foundations as an online marketplace to provide ease-of-use and a seamless on-demand experience. In the cloud wars landscape, AWS is like the U.S. military of the public cloud – bigger than all the others combined. AWS is very streamlined in how it offers its products, how they can be purchased and how fulfillment is handled. AWS’s ability to provide a user-friendly e-commerce experience isn’t a surprise, because, after all, AWS is Amazon. Just like you can buy a book or a suitcase from Amazon.com, you can buy compute time or storage. AWS also boasts sophisticated large-scale data base products, APIs and control structures, including AWS Lambda, which lets customers run code without provisioning or managing servers. Given Amazon’s core value of customer service, the rapid growth of AWS and its ease of use make sense.
Google Cloud Platform
While Google’s cloud offering is newer on the scene, its recent enterprise investment makes it one to watch. The company’s history of being forward-thinking and cutting edge is carried throughout its cloud platform value prop. At its NEXT conference in March, Google focused its keynotes on the sophistication of its technology, and how savvy developers could take advantage of Google Cloud Platform’s dynamic point-to-point networking, artificial intelligence and machine learning offerings. Google arguably has the most sophisticated developer cloud on the market, which makes sense as one of the large-scale inventors of web scale, or scale-out computing.
IBM, which combined its July 2013 acquisition of SoftLayer Technologies with its IBM SmartCloud to form the IBM Cloud Services Division, is building on its historical strength as a provider of consulting and computer services. Professional services and consulting is not necessarily top of mind for AWS or GCP, so IBM’s global cloud platform appeals to those customers looking for a stronger engagement model. Professional services is part and parcel to IBM, and it can combine its cloud offerings and consulting capabilities with other technologies such as Watson.
Rounding out the four, Microsoft’s Azure has a strong and steady history in the CSP market. Though it offers many of the same features as the other three “big guns,” it has stood out by intelligently leveraging its long-standing strength in the enterprise and bridging its existing enterprise capabilities with its newer cloud offerings. Customers can run other Microsoft software offerings such as Exchange, SQL Server or Active Directory both in the cloud or on premises. Microsoft also offers licensing incentives for existing enterprises customers as an option to help them embrace Azure.
If your organization is, for example, a next-generation application outfit and has been in the cloud from the get-go, then AWS or GCP will suit you just fine. If you’re generating an Internet-of-Things-based application with a mobile front end, for instance, that will run on Android and iOS and you’re storing a lot of data, any one of the four will handle the job admirably.
But here’s where it gets a little tricky: if you’re an enterprise customer with your own (or leased) data center(s) and Microsoft applications and backoffice applications processing lots of data (a bank, for example), it’s a much bigger proposition to move to the public cloud. This type of customer is currently a challenge for all four of the major CSPs. For organizations with a more traditional IT infrastructure, it’s not a matter of simply copying and pasting their applications and technology into the cloud and calling it a day.
A Demilitarized Zone: The Rise of the Multi-cloud Strategy
Partly in response to these complexities, we’ve seen the rise of hybrid cloud and multi-cloud architectures and approaches. Multicloud, in particular, has been an unexpected twist in enterprise cloud adoption. CSPs catalyzed enterprise cloud adoption by driving prices lower and enhancing the sophistication of their offerings, yet now this very same competitive dynamic is allowing businesses to choose different clouds for different workloads based on the strength of each CSP.
There’s also the age-old concern about having a single supplier and being subject to vendor lock-in. By adopting a multi-cloud approach, enterprises can avoid the “data gravity” problem: as data accumulates there is a greater likelihood that more and more additional services and applications will be attracted to this data. By keeping data in different clouds, enterprises can avoid arduous and difficult data migration while taking advantage of differing pricing structures among the CSPs.
In the end, yes, we’re going to see an intensification of the cloud wars among the four big public cloud providers – Amazon, Google, IBM and Microsoft – and that’s a good thing for enterprises moving to the cloud. This competition is at once driving down prices, increasing buyer options and inspiring innovations in IT architecture that will ultimately lead to more freedom of choice and the ability to purpose-build cloud environments for enterprises who are looking to the cloud(s).
Google recently announced details of a chip specifically built for machine learning in the form of its Tensor Processing Unit (TPU) that has operated in stealth mode within Google’s data centers for over a year. TPU delivers superior performance for machine learning use cases by processing “more operations per second into the silicon” and is designed to work with TensorFlow, Google’s open source library of machine learning applications. TPU requires fewer transistors per operation and can optimize performance per watt by an order of magnitude for machine learning applications and use cases in what amounts to “fast-forwarding technology about seven years into the future (three generations of Moore’s Law),” according to a Google blog post. TPU currently powers applications such as Google Street View, RankBrain as well as Google Maps and powerfully illustrates not only Google’s commitment to machine learning technologies but also its competitive differentiation as a vendor with the ability to design and operationalize hardware optimized for machine learning applications.
Google recently announced the integration of BigQuery, its fully managed data warehouse that allows customers to store and query petabytes of data using SQL and a utility-based pricing model, with its consumer facing Google Drive application. As a result of the integration, users can query files stored in Google Drive directly from the BigQuery user interface, without loading them into BigQuery. Moreover, users can save the results of queries on Google Sheets directly into Google Sheets, and update those queries as data within Google Sheets dynamically changes. The integration between Google BigQuery and Google Drive breaks down the barrier between the Google Cloud Platform and its Google Apps suite and correspondingly gives Google Cloud Platform customers a more seamless, integrated experience with respect to the ability to query data that resides outside of BigQuery. More importantly, the integration provides Google Drive users with an extra incentive to tap into the lightning fast SQL queries of BigQuery and explore its capabilities for querying data as a prelude to a more sustained investigation of its ability to analyze massive datasets and its impressive integrations with third parties such as Tableau, Talend and Qlik.
The Google Cloud Platform experienced a major outage marked by the loss of “external connectivity in all regions” in all regions on April 11 lasting roughly 18 minutes. Caused by a networking failure, the outage impacted all regions and represented one of the most systemic outages in the history of the public cloud particularly given how AWS and Microsoft Azure have suffered through outages specific to one or more availability zones, but never in all regions. Since the outage and an attendant post-mortem analysis of its root causes, Google claims to have resolved the issues with its network configuration software, and taking a cue from its competitor Amazon Web Services, served up a remarkably detailed post-mortem analysis of the outage and its origin, chronology, escalation pathways, resolution and near and long-term remediation. The larger point, here, however, is that despite its recent windfall of customer signings featuring the likes of Apple, Spotify and the Home Depot, the Google Cloud Platform is still in the process of ironing out the kinks in its IT infrastructure as they relate to process, technology, alerts, notifications and root cause analytics. The outage constitutes both a reflection on the evolution of the Google Cloud Platform and the continued immaturity of data-driven alerts and notifications despite the efflorescence of contemporary technologies dedicated to intelligent automation, self-optimized infrastructures and real-time analytics on streaming data. The lesson learned from Google’s recent outage is that the integration of the mitigating, data-driven checks and process automation steps designed to identify and swiftly ameliorate issues within the Google Cloud Platform, as they arise, still have yet to mature to the point where they are capable of isolating problems to a specific availability zone or cluster of availability zones in contrast to a categorical and unprecedented cascade across all regions. As such, the outage raises more questions than it does answers about the architecture undergirding Google’s Cloud Platform as well as how software configuration glitches can have unexpectedly far reaching consequences despite the surfeit of contemporary analytic capabilities available to proactively monitor the health of IT infrastructures.
Google recently announced the alpha release of Cloud Machine Learning, a managed, cloud-based framework for building machine learning models by using the TensorFlow framework that undergirds products such as Google Photos and Google Speech. Google’s Cloud Machine Learning platform features a Cloud Vision API that can categorize images into over a thousand categories such as “tree,” “book” and “car” and additionally identify “individual objects and faces within images” as well as read print within images. The platform also features a Cloud Speech API that can transcribe speech into text by using neural network models. Moreover, Google Cloud Machine Learning contains a Google Cloud Translate API that can translate source language into a supported target language, such as French to Japanese, for example. Google Cloud Machine Learning integrates with Google Cloud Dataflow in addition to data stores from Google Cloud Storage and Google BigQuery. By offering pre-trained machine learning models in conjunction with the capability to build customized models for specific scenarios and use cases, the platform delivers predictive modeling capabilities that can scale to support terabytes of data and rapidly proliferating data sources. Google Cloud Machine Learning competes with Amazon Machine Learning and Hewlett Packard Enterprise Haven On Demand, the latter of which is now commercially available on the Microsoft Azure platform. The alpha release of Google Cloud Machine Learning further illustrates Google’s investment in its Google Cloud Platform and the depth of its commitment to building an increasingly competitive position in the contemporary cloud computing market landscape.