Key Cloud Capabilities Comparison: Microsoft Leads Across IaaS, PaaS, Storage and More

Editor's Notes

1. The cloud powers modern enterprise experiences. In many cases the technology in an enterprise can be a differentiator or competitive advantage. There is no lock in like cloud lock in.
2. 34 Azure regions around the world.
3. Storage options for Azure and AWS users are varied. Both AWS and Azure offerings are roughly equivalent to each other. Azure users have choices of Block blob, File, Page blob and disk, and table and queue storage types. And for data redundancy, you have locally redundant storage (LRS), geo-redundant storage (GRS), Read Access GRS (RA-GRS), or Zone Redundant Storage (ZRS) from which to choose. The data redundancy type options are based upon which storage type you select. You can also select your storage geographic region from a dropdown list, but be aware that some regions and redundancy types are more expensive than others. AWS customers have several storage options and multiple choices to make. Amazon’s Simple Storage Service (S3) is a very popular choice for storing and retrieving data from anywhere on the web and is the basis for many third party commercial cloud storage services such as Dropbox. AWS also offers CloudFront to distribute your static and dynamic web content. AWS Elastic Block Store (EBS) is block storage that’s appropriate for database workloads. You can also encrypt this block-level storage as an added feature. Amazon Elastic File System (EFS) is in preview, but it is a file storage for EC2 virtual machines (VMs) that allows you to create, mount to your EC2 instances, and then read and write data from your VMs and to and from your filesystem.
4. Up until this point, federal agencies could only migrate low and moderate impact workloads. Now, Azure Government has controls in place to securely process high-impact level data—that is, data that, if leaked or improperly protected, could have a severe adverse effect on organizational operations or, assets, or individuals. To further extend our commitment to providing high levels of security controls and compliance required for government data, Azure Government is adding two new regions for US Department of Defense data, designed to meet DISA Impact Level 5. A first of their kind, these regions, to be designated US DoD East and US DoD West, are architected to meet stringent DoD security controls and compliance requirements, and will be specifically dedicated to DoD workloads and data at Level 5.
5. What’s the same All of the services offer a means to connect private networks to cloud networks over a leased line rather than using the Internet. That’s pretty much where the similarity ends. What’s different – AWS Direct Connect is a 802.1q VLAN (layer 2) based service[3]. There’s an hourly charge for the port (that varies by the port speed), and also per GB egress charges that vary by location (ingress is free, just like on the Internet). What’s different – Azure ExpressRoute is a BGP (layer 3) based service, and it too charges by port speed, but the price is monthly (although it’s prorated hourly), and there are no further ingress/egress charges. An interesting recent addition to the portfolio is ExpressRoute Premium, which enables a single connection to fan out across Microsoft’s private network into many regions rather than having to have point-to-point connections into each region being used. What’s different – Google Direct Peering is a BGP (layer 3) based service. The connection itself is free, with no port or per hour charges. Egress is charged for per GB, and varies by region. Summary table Cloud Type Port Egress Amazon VLAN $ $ Microsoft BGP $ Google BGP $
6. MSFT bought Xamarin,
7. Secondary Indeces... Server-side scripts in JavaScript, Triggers, Bounded Staleness, Session Consistency, ACID transactions within a collection through stored procedures, Access rights can be defined down to the document level
8. There is no clear winner for now, as each service has its strengths and weaknesses. MS Azure supports the widest variety of data sources and formats. I would argue MSFT has the best Data Preprocessing coverage… They have the best variety on data sourcing and more flexibility IMO on data preprocessing. This also allows me to transform the data using MS Azure quickly – but in most cases I would do my own transformation using my own tools. Azure gives me full, control and allows me to exploit my data’s specific traits to perform operations in the most efficient way. Google has incremental training. So if you have tons of data that might be very appealing. In essence it allows you to use virtually infinite data. Taking the effort of assessing when to retrain a model away (or at least simplifying it). Azure also has the widest range of model choices. AWS is linear model only and Google is similar (by choice).
9. Microsoft Azure edges Amazon AWS primarily due to the following factors: A more mature stream analytics platform that supports SQL based analysis of stream data (Amazon's version is not too far) A Machine Learning Algorithm marketplace along with a data marketplace A mature data visualization service in PowerBI (Amazon's QuickSight is in preview currently) A robust ETL framework in Azure Data Factory The existence of Azure Data Lake Store which is a brand new HDFS compatible hyper scale repository and can be used natively by a wide variety of cloud data services without having to move data around (as would need to be done say if I choose to store data in S3 and use Redshift)