Do you know that the global business community will be spending $310 billion on the Internet of Things (IoT) by 2020?
Isn’t it a huge investment! Why is this trend booming?
Actually, the IoT has encapsulated devices. The software, sensors, actuators and connectivity through vehicles and home appliances are penetrating deeply. This networking model is skyrocketing in almost every walk of life. That’s why small entrepreneurs to big industrialists are hungry for the digital connectivity, interactivity and exchange of data over the internet.
Its reason is intelligence that underlies a massive set of data. The influx of data is expanding. If you possess the lens of an experienced data scientist, you can extract and mine that business intelligence via digging out patterns. Simply put, whatever size of data the IoTs produce, you can churn it in a few minutes using data mining software and tools.
Here, the biggest challenge is the progressive nature of data size. It’s massively sizing into amassed data. This is where the cloud computing comes in play.
How is IoT connected with cloud computing?
I won’t be wrong, if I say that the cloud computing and IoT are closely paired. On one hand, the latter technology is piling a mountain of data, whereas the cloud computing is empowering them. As a virtual assistant, it is helping in processing the scaling up data. What state-of-art IT infrastructure the cloud provides, it’s irrespective of boundaries and space.
· Cloud Computing:
In other words, the cloud computing allows you to carry on computing tasks of business activities, data analysis or mining over the internet.
It’s a mutation that manipulates time and space constraint workplace. Let’s simplify what I want to convey. If I want to reset or format my mobile device, I need to transfer the pan data from its movable (memory card) and immovable memory (ROM). But, the question is ‘Where’; where should I transfer it?
Here, Google drive offers its cloud space. You can browse and trigger data migration over the internet. Subsequent to phone formatting, you can re-install that data anytime. You can heave a sigh of relief as its remotely located Google server manages your data.
Scope of Cloud Computing:
The cloud computing, indeed, minimizes the memory or space constraint. Because of its vitality, security and turnaround time, many reputed industries from all domains rely on it. Instead of relying on a space-bound domain hosting, this computing delivers a frictionless paradigm to store big data and analytics.
Don’t you believe? Go through the list of these top cloud computing products that serve the cup of tea of data scientists:
List of Top Cloud Computing Products for Data Scientists:
· AWS Elastic Compute Cloud (35%)
· Google Compute Engine (20%)
· AWS Lambda (15%)
· Azure Virtual Machines (13%)
· Google App Engine (13%)
· Google Cloud Functions (11%)
· AWS Elastic Beanstalk (7%)
· Google Kubernetes Engine (6%)
· Azure Functions (5%)
· AWS Batch (4%)
· Azure Container Service (4%)
· IBM Cloud Virtual Servers (3%)
· IBM Cloud Foundry (3%)
· Azure Kubernetes Service (2%)
· Azure Batch (2%)
· IBM Cloud Container Registry (1%)
· IBM Kubernetes Service (1%)
· Azure Event Grid (1%)
Leading cloud computing services:
· Amazon Web Services (40%)
· Google Cloud Platform (25%)
· Microsoft Azure (20%)
· IBM Cloud (6%)
· Alibaba Cloud (3%)
Big Data Analytics in Cloud Computing:
The cloud provides with immunity to store and analyze data of any size on the World Wide Web. The space and time constraints don’t appear as a roadblock. Thereby, if your data repository gets relentless inflow of sensory data, cloud is the best virtual assistant to let you analyse data without worrying about time and storage. It assists you:
To manage data by self and on-demand: You don’t have to seek special permission, as it is accessible online. However, the security layers will be there to prevent data from vulnerability. Thereby, the first brick is laid to ground up analytics report.
To access global network: You can get an easy access to AWS or Google Service or any other intended one from Asia, Europe or any continent. It means that a virtual assistant can outsource its predictive or comparative data analysis as an outcome of the secondary market research. Also, there is no constraint to call in or call out data on any laptop/ mobile phone/ tablets.
Share-ability: Resource-pooling is all about sharing various data resources from diverse locations. Google, let’s say, shares its cloud space to upload photos on the Google Photo apps. It allows multi-tenancy, which has its root in 1990s with the name Time-Sharing. While leveraging multiple users, its share-ability assigns the cloud address in a surveilled environment.
Flexibility to expand: Since there is no limit, the cloud lets you to scale up its services according to requirement. You can capitalize on its elasticity by opting for the intended software/platform/bandwidth or calling it off. Thereby, you get a stronghold on the access to resources for the seamless analysis process.
Pay for performance: It’s a value-based purchasing model. It means that you can presume usage statistics, be it for the storage or processing or bandwidth. Besides, it offers pay-per-use service. Let’s say, you have employed it for communicating with the desktops and the internet only. As its communication network includes mobile phones, its cost scrolls up.
Which is better-Cloud Computing or Data Science?
Apart from these, cloud always gives a strong competition to the data science. The data scientists are aware of diverse viabilities of this computing and data science in relation to data analysis. This comparison will terminate all confusions regarding both:
For remote computing
For studying extremely large data sets
Derive business intelligence
Availability of end-to-end computing facilities via PaaS, SaaS, IaaS over the internet.
Dealing with velocity, volume and variety of data
When IT requirements and app usage scale up and the need to maintain centralized access is there, it becomes a necessity.
When analysis of voluminous data (in petabytes) requires distributed framework with computing, data science comes into play.
Economical solution for data analysis/storage, instances-proof centralized platform with zero upfront cost, like IaaS, PaaS, Saas.
Easy to take on effective study on scaling data, cost-effective, robust environment, like Hadoop, Apache & MapReduce.