Cloud has become one of the leading markets in the IT industry. More and more business, as well as professionals, have shifted their work over to the cloud. Why shouldn’t they? There are many advantages of relying on cloud technology.
Typically, public cloud services are used as:
As per the Gartner Survey Report, the market of public cloud is estimated to hit a total worth of $411 bn by 2020. Cloud technology is used for making backups, storing data, working in co-op, and whatnot.
At present, Amazon Web Services, Google Cloud, and Microsoft Azure are the big three of the cloud technology. Which one is the best? Well, there is no definite answer.
However, here is a comparison between these three biggest names in public cloud computing to help you decide the winner for yourself.
Any efficient cloud provider flaunts the ability to scale thousands of nodes in a mere couple of minutes. Amazon uses EC2 (Elastic Compute Cloud) to provide users with computing services for configuring VMs using custom as well as pre-configured AMIs.
Other than the total number, power, memory capacity, and size of VMs, users can also choose among the various regions and zones available. Additionally, EC2 also provides support for auto-scaling and ELB (load balancing) features.
The former feature allows distributing loads across instances, while the latter feature enables automatic scaling of the VM capacity. While Azure offers VHD (Virtual Hard Disk) for configuring VMs, Google comes with the GCE (Google Compute Engine) for doing the same.
A VHD can be predefined, by Microsoft or third-parties, or user-defined. With each VM from Microsoft Azure, users also need to specify the number of cores and amount of memory required.
Like AWS, Google allows users to pick from available regions. Launched in 2013, GCE is available with a multitude of features, such as faster persistent disks, load balancing, varied OS support, live migration, and additional cores.
Amazon Web Services offers ephemeral storage. It is allocated as soon as an instance is started and destroyed when the instance terminates. AWS offers Block Storage, which is equivalent to hard disks, and, hence, can be attached to any instance or used separately.
For object storage, AWS offers S3 Service, while it comes with Glacier for archiving services. AWS completely supports NoSQL databases, relational databases, and big data.
Microsoft Azure relies on the D drive and Page Blobs for VM-based volumes. While the former is the temporary storage option, the latter is Microsoft’s block storage option.
With Windows Azure Table and HDInsight, Microsoft Azure provides support for relational and NoSQL databases as well as big data.
Google Cloud Platform also offers temporary as well as persistent disk storage. Google Cloud Storage is available for Object Storage. The cloud provider offers support for Big Query, Big Table, and Hadoop.
Google Cloud supports relational databases via Google Cloud SQL and offers inexpensive archiving with no latency on recovery via Google Cloud Nearline. Normalized databases are used by all the big three of public cloud computing.
In addition to offering cloud services, cloud providers also cater to development tools requirements. Such tools help in building, debugging, deploying, diagnosing, and managing multi-platform scalable apps and services meant to be used as a cloud service.
Google Cloud offers Cloud Test Lab and Cloud Source Repositories for App Testing and Git Repositories, respectively. However, it doesn’t offer DevOps, Developer Tools, and Media Transcoding, which Amazon Web Services and Microsoft Azure deliver.
Amazon Web Services comes with Elastic Transcoder, Device Farm, and CodeBuild for Media Transcoding, App Testing, and DevOps, respectively. Microsoft Azure offers Media service for Media Transcoding, DevTest Labs for App Testing, and Visual Studio Team Services for DevOps.
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In the midst of this pandemic, what is allowing us unprecedented flexibility in making faster technological advancements is the availability of various competent cloud computing systems. From delivering on-demand computing services for applications, processing and storage, now is the time to make the best use of public cloud providers. What’s more, with easy scalability there are no geographical restrictions either.
Machine Learning systems can be indefinitely supported by them as they are open-sourced and within reach now more than ever with increased affordability for businesses. In fact, public cloud providers are increasingly helpful in building Machine Learning models. So, the question that arises for us is – what are the possibilities for using them for deployment as well?
Model building is very much like the process of designing any product. From ideation and data preparation to prototyping and testing. Deployment basically is the actionable point of the whole process, which means that we use the already trained model and make its predictions available to users or other systems in an automated, reproducible and auditable manner.
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Having taken over our daily communications almost entirely, the web is constantly evolving and expanding. With this continuous expansion, it provides you with more and more data that you consume daily. However, all the information that appears on the web, every like, subscription, tweet, downloaded video, or uploaded vlogs, making all of this possible requires a highly sophisticated computational process working behind it, constantly.
The smooth functioning of the web has more to it than just the computational procedure. Current web services operate through a solid and intricate network of RAM, containers, database engines as well as machine learning skills.
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A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
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The world of data analytics and technology have been dramatically altered by cloud computing. The two companies which are known for providing tremendous cloud computing technologies are- Google Cloud Platform and Amazon Web Services.
This artcile highlights the comparison between these big companies.
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Cloud computing has brought about a new wave of change in the industry, especially for enterprises and organizations that are continually growing. Thanks to cloud computing platforms, you no longer need to struggle with on-premise servers’ limitations and manage a complicated hardware infrastructure. Cloud computing takes care of all your enterprise storage and computing needs in a much more cost-efficient and secure manner.
Globally, the cloud computing market is dominated by three giants – Azure, Google Cloud, and AWS. Today, we’re going to pit Google Cloud and AWS to get a neck-to-neck comparison of these two cloud computing platforms.
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