Google cloud features evolve to turn up the heat on AWS
Google made several moves in 2017 in an attempt to close the gap between itself and market leader AWS. But will those efforts be enough to win over new customers?
Throughout 2017, new Google cloud features emerged that took direct aim at public cloud leader Amazon Web Services. But, at the same time, another cloud player -- Alibaba -- continued to rise in the international market, and Microsoft Azure became the clear second-place contender behind AWS.
So, where exactly does Google's cloud platform stand?
Here's a look at three Google cloud features unveiled in 2017, how they compare to similar AWS offerings and what work remains for Google to capture more cloud market share.
Google Transfer Appliance
Google's response to AWS' Snowball for physical data migration is its Transfer Appliance service. It's a rack-mounted, high-capacity storage server that users install in their data center, fill up with data and ship back to Google. When Google receives the physical device, it uploads that data to Google Cloud Storage.
Google Transfer Appliance's capacity is pretty impressive, with the ability to compress data up to 1 petabyte. Like Snowball, it offers an alternative to the slow data transfer rates an organization faces when it attempts to move data over the internet and into the public cloud. Moreover, it encrypts enterprise data at the time of capture to ensure that data is unreadable if the physical device is lost in transit.
Managed instance groups
To compete against AWS' CloudFormation service, Google rolled out managed instance groups, a service that enables you to launch and shut down a collection of machine instances that share the same configurations.
With managed instance groups, admins can manage multiple VMs -- within either one or multiple Google Cloud Platform availability zones -- as a single entity. Like CloudFormation, the service also supports autoscaling, load balancing and rolling updates.
In addition to infrastructure services, emerging technologies, such as machine learning, are now a battleground for public cloud providers. And while most enterprises are just getting their feet wet with machine learning and AI services, demand will skyrocket as adoption matures.
There are now several Google cloud features aimed at machine learning, including Cloud Dataprep, Dataflow and BigQuery, which help organizations manage and analyze structured and unstructured data. Google, along with AWS, also provides TensorFlow, a machine learning engine that you can use without being an AI expert.
This combination of data, data engines and self-learning systems provides businesses with the ability to gain better insights from data they've gathered for years.
Last year, AWS also unveiled a number of machine learning and AI options, including SageMaker, a managed service that comes with 10 popular machine learning algorithms and is also designed to reduce the AI learning curve for nonexperts.
Despite the recent surge in new Google cloud features, AWS has kept pace. And, clearly, AWS' strategy is to quickly build out its portfolio and release services in quantities that Google and Microsoft struggle to match. My guess is that other cloud players are waiting for AWS to trip, but the cloud market leader shows no signs of an upcoming stumble.
Meanwhile, new players, such as Alibaba, have come on to the scene. According to some analyst firms, Alibaba is now the new third biggest infrastructure-as-a-service vendor in the world, displacing Google.
One likely outcome is that Google eventually falls back to being a more niche enterprise player. Google will continue to focus more on cutting-edge technology and less on core infrastructure services, such as storage and compute, that are clearly in the wheelhouses of both Microsoft and AWS. Ultimately, this could make Google more relevant in areas such as AI, at the cost of not attempting a battle with AWS and Microsoft on a feature-by-feature basis.