Almost all cloud market discussions start with Amazon Web Services leading the industry. And while it's true AWS...
has a strong offering and leads in market share, it doesn't necessarily mean it's your best option. If you work with big data, use hybrid cloud or have a substantial software platform with Microsoft tools, you may want to re-evaluate your cloud vendor selection. Microsoft Azure and Google Cloud are two solid AWS alternatives, but which is right for your organization?
AWS, Google Cloud and Microsoft Azure each offer infrastructure as a service (IaaS) and platform as a service (PaaS). Additionally, all three cloud providers offer competitive prices for on-demand computing and storage resources. But while AWS has a reputation for rapidly rolling out new services -- from high-volume stream processing to synchronizing data between mobile devices and other platforms -- Google and Microsoft aren't just sitting on their hands.
Microsoft is bringing its Azure Machine Learning to a wide audience. And soon, adept Excel users may be adding Machine Learning experience to their résumé. Meanwhile, Google's Datastore and click-to-deploy Hadoop bring large-scale NoSQL and big data into the "as a service" realm. AWS has comparable offerings, but Google's ease of use and feature quality is tough to beat.
Advantages of Google Cloud
If you've searched the AWS console to change a parameter, you've likely noticed it's designed differently than Google's console. Google's cloud interface is clean and easy to navigate.
Another Google Cloud advantage is its rapid load balancer deployment. In some situations, AWS recommends pre-warming load balancers, and users must inform the vendor about the expected traffic load. Google, however, does not require this.
Google's click-to-deploy options make it easy to rapidly deploy stacks of tools, including LAMP, Ruby and Hadoop. Users can also deploy Hadoop by specifying a handful of parameters, such as instance size and number or data storage location. To deploy Spark on Hadoop, users simply click on a check box. Google Cloud Dataflow, an ETL and workflow management service, is available for both batch and streaming ETL.
Google App Engine is another useful deployment tool. App Engine is a high-performance PaaS that supports Python, Java, PHP and Go programming languages. The platform works with variety of storage options, including Cloud SQL, Datastore and Blobstore. And, perhaps as no surprise, the engine also provides search API access.
Rapid scalability and deployment, in addition to easy-to-use big data tools, makes Google Compute Engine and App Engine appealing for big data and analytics.
Advantages of Microsoft Azure
Similar to AWS, Microsoft aggressively adds features to its cloud platform. Two main focus areas are the vendor's hybrid cloud support and its machine learning as a service offering.
AWS supports hybrid cloud deployments, but Microsoft's popular server operating system gives it a distinct advantage. The combination of Microsoft Azure, Windows Server and Microsoft System Center provides cloud and on-premises administrators with a consolidated platform for managing hybrid cloud components.
Microsoft System Center offers cloud management features, such as infrastructure provisioning and performance monitoring. Its App Controller also provides a unified look at infrastructure across on-premises or Azure systems. Tight coupling with Visual Studio also appeals to Windows developers, especially those working in DevOps environments.
StorSimple, Microsoft's hybrid storage service that includes primary, backup and archive storage, further supports hybrid clouds. Users access StorSimple through an Azure-based portal. Microsoft's hybrid storage service uses a storage area network (SAN) infrastructure. Additionally, StorSimple offers disaster recovery to any data center. To secure data moving through StorSimple, the service uses an AES-256 encryption.
Another Microsoft Azure cloud advantage is its Machine Learning service. Collecting large volumes of data is only useful if analysis is applied, but Microsoft successfully applies machine learning advances to its own business and its advanced analytics are available to a wide audience. The service includes Machine Learning Studio for creating and evaluating machine learning models. Azure Marketplace packages allow users to avoid starting from scratch. Pre-made packages can predict customer churn or analyze a company's social media content.
If you're already invested in the Microsoft platform and building a hybrid cloud, Azure is a good option. It also appeals to users that need advanced analytics, but lack the statistics or computer science background to start from scratch.
Although AWS has a lot to offer, don't discount Microsoft Azure and Google. Both Azure and Google offer unique features and advantages that could suit your cloud needs better than AWS. But the only way to know is to take a closer look.
About the author:
Dan Sullivan holds a master of science degree and is an author, systems architect and consultant with more than 20 years of IT experience. He has had engagements in advanced analytics, systems architecture, database design, enterprise security and business intelligence. He has worked in a broad range of industries, including financial services, manufacturing, pharmaceuticals, software development, government, retail and education. Dan has written extensively about topics that range from data warehousing, cloud computing and advanced analytics to security management, collaboration and text mining.
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