With the advancement of technology, companies have been able to produce and analyze more and more data every day. Large amounts of data are spewed out every second. Data analysis has spread into so many different fields and so the world has been introduced to many advanced concepts like machine learning and artificial intelligence.
In order to build a brilliant ML or AI model, we need clean data, and we need tons of it. The more the data, the better the output, and the more cleaner the data, better the efficiency of our model. The larger the size of the data, the more difficult is its storage and management and that’s where CLOUD comes in. Before we dwell on the influence of cloud on analytics, we must understand what a cloud is!
What exactly is the CLOUD?
A Cloud is basically a collection of servers that are used to store and retrieve data with the help of the internet. It helps users access files/data from any point using any device. You probably use the cloud almost every other day. With the present generation smartphone, we have the option to store files and information on the cloud rather than on the device. This helps us to access these files by logging into the cloud with the help of the internet on any device.
The servers are connected to the internet and are hosted by what is called data centers. (Those cool rooms with black boxes and blinking lights that you might have seen in many Hollywood movies.)d blinking lights that you might have seen in many Hollywood movies.)
There are basically four types of cloud computing models:
- Software as a Service (Saas): Software as a service is when the application is hosted onto the servers and the clients access them using the internet rather than downloading it to their device. Eg: AWS, Slack, Google Apps, Dropbox, Gmail, etc.
- Platform as a Service (Paas): Instead of buying a whole car, you buy the different parts and tools required to build your own. This is what is meant by PaaS. Here the paas vendors provide the clients with all the necessary tools and requirements needed to build your own application. Eg: Microsoft Azure, Google App Engine, Apache Stratos, etc.
- Infrastructure as a Service (IaaS): When the clients rent out the servers from data centers to build their own application, it’s called IaaS. Eg: Amazon Web Service (AWS), Microsoft Azure, Cisco Metacloud, etc.
- Function as a Service (FaaS): Iaas is also known as serverless computing, is when the whole process of development in the cloud is broken down into smaller parts and the portions in development are run only when needed. Eg: AWS Lambda, Microsoft Azure, OpenFaas, etc.
Influence of Cloud on Data Analytics
- Storage and Safety. Ease of storage of data is one of the biggest plus points of cloud based analytics. Storing large amounts of confidential company data on a hard disc or personal device makes it vulnerable to accidental deletion or even exploitation of confidentiality by anyone at any time. Storing data's into the cloud with the help of internet make it easier to access the information from any point and also keeps them safer (from power cuts or system failure etc.)
- Ease of access to data. Cloud makes it easier for analysts to access data specially when working from a remote location. Imagine storing large amounts of data on your personal hard drive or buying a hard disc just to store all these data's. It makes the process more tedious and also vulnerable. When the data is stored in the cloud, any employee can access the data when needed with the help of internet.
- Cost Flexibility. Storing, managing and maintenance of data by any organization is always a fund draining idea. Moving one’s data into the cloud reduces the cost data handling to a major degree. You don't need to purchase the equipment's, pay for the upgrades or hire an expert. Cloud computing provides various cost categories depending on the resources you need and the resources you use.
- Machine learning, artificial intelligence, Internet of Things. More complex processes like MI and AI requires high level of data in order for us to get a model of maximum efficiency. The demands raised by such complex procedures makes it apt for the cloud. We can expect much more faster data management and output generation with the help of cloud.
There are TONS of advantages of using cloud for your data management. For any number of cases like machine learning or artificial intelligence or predictive modelling, Cloud might be one of the best resource that can be used for the maximum gain of any organization.