Cloud Computing and Big Data

Cloud Computing and Big Data

The difference between cloud computing and big data-high tech

Cloud computing works in an integrated way, and big data is under cloud computing. The crucial difference between cloud computing and big data is using cloud computing to handle huge storage capacity (big data) by expanding computing and storage resources. On the other hand, big data is nothing more than a huge amount of unstructured, redundant, noisy data and information that needs to be extracted for helpful knowledge. To perform the above functions, cloud computing technology offers a variety of flexible ways to work with vast amounts of data.

This includes the input, processing, and output models described below. This figure details the relationship between cloud computing and big data.

Comparison table

Definition of cloud computing

Cloud Computing Provides an integrated platform of services for storing and retrieving any amount of data on-demand, anytime, anywhere, using the high-speed Internet. The cloud is a wide set of terrestrial servers distributed throughout the Internet for storing, managing, and processing data. Cloud computing is designed to make it easy for developers to implement web-scale computing. Since the Internet is the foundation of cloud computing, the evolution of the Internet has created a cloud computing model. A high-speed internet connection is required for cloud computing to work efficiently. It dynamically adds capacity and functionality, providing a flexible environment that can be used according to a pay-as-you-go strategy.

Cloud computing has several important characteristics: resource pooling, on-demand self-service, wide-ranging network access, measured services, and rapid resilience. There are four types of clouds: public, private, hybrid, and community.

There are basically three cloud computing models. Use hardware and software services with PlatformasaService (Paas), Infrastructure as a Service (Iaas), and Software as a Service (Saas).

1. Infrastructure as a Service – This service is used to provide storage capacity and infrastructure, including virtual machines. Implement resource virtualization based on service level agreements (SLAs).


2. Platform as a Service – Located above the IaaS layer, it provides a programming and runtime environment that allows users to deploy cloud applications.


3. Software as a Service – Delivers applications that run directly at the cloud provider to clients.

Definition of big data

Data will be as follows: Increasing volume, diversity, and speed beyond the capabilities of big data IT systems can make data storage, analysis, and processing difficult. Some organizations have developed equipment and expertise to handle this type of large amount of structured data, but the exponentially increasing data and rapid data flow is my and practical intelligence. Is generated quickly. This vast amount of data cannot be stored on regular devices or distributed in a distributed environment. Big Data Computing, Data Science This focuses on multidimensional information mining for scientific discovery and business analysis in large-scale infrastructure.

The basic dimensions of big data are the quantities, speeds, diversity, and truths mentioned above, followed by the further evolution of the two dimensions of variability and value.

• Volume– Indicates that the size of the data already has problems processing and storing the data is increasing.


• Velocity– This is the instance where the data was captured and is the velocity of the data flow.


• Variety– Data is not always displayed in a single format. For example, there are various formats of data such as text, audio, images, and video.


• Credibility– Called data reliability.


• Variability– Describes the reliability, complexity, and inconsistencies generated by big data.


• Value– The original format of the content may not be very useful and productive, so the data is analyzed and valuable data is discovered.

Relationship between cloud computing and big data

The figure below shows the relationship and behavior of cloud computing with big data. This model uses the primary input, processing, and output computing models as references, and uses input devices such as mice, keyboards, mobile phones, and other smart devices to insert big data into the system. The second phase of the process involves the tools and techniques used in the cloud to provide the service. Finally, the result of the process is delivered to the user.

Big data and cloud computing

Big data utilization example 1: Financial field (Fintech)

Let's take a look at an actual use case of big data. There are conspicuous movements to aim for innovation in the financial field by utilizing big data, and these movements are collectively called "FinTech".

The "White Paper on Information and Communication" issued by the Ministry of Internal Affairs and Communications classifies FinTech into four types: settlement/remittance, asset management, financing/procurement, and blockchain. Typical examples of payments and remittances include PayPal, which allows you to send money without telling the other party your personal card number or account number, and Apple Pay, which allows you to make payments on your iPhone.

Examples of asset management include THEO, which uses algorithms to provide asset management advice, and Money Forward, a personal household account book creation app.

Examples of financing / procurement include crowdfunding services such as READYFOR and social lending. Blockchain is a technology that encrypts financial transaction records and processes and records them in a decentralized manner.

It is the base technology for virtual currencies (cryptocurrencies) such as Bitcoin and has been tested in Japan since 2017.

Big data utilization example 2: Automobile

Telematics and self-driving cars are attracting attention as examples of big data in the automobile industry.

Telematics refers to a system that provides information services such as traffic jam information and weather forecasts by connecting a smartphone or car navigation system to a car.

In addition to using GPS, we are trying to collect data such as vehicle position and speed, traffic records, and traffic volume from the driving situation of the car and provide personalized information to each car.

brand experience in customer satisfaction and customer loyalty

An example of telematics is Toyota's "T-Connect" navigation system. This consists of three services: a service that conveys road surface conditions and traffic congestion information by voice, a service that allows you to add apps such as Gurunavi to the navigation system, and a service that notifies you of route guidance and emergency information.

Both aim to improve driving safety and convenience by exchanging data with individual cars in conjunction with car navigation systems. Second, self-driving cars are those in which a computer automatically drives a car, not a human driver.

In the United States, major IT companies and automobile manufacturers are playing a central role in the development and test driving of self-driving cars.

Artificial intelligence collects information such as road surface, weather, temperature, and traffic jams, and automatically drives the car. However, it has not been put into practical use as of 2018. In addition to the technical hurdles, there are legal hurdles such as liability in the event of an accident and the range of roads on which self-driving cars can travel.

Big data and cloud computing

Big data use case 3: Healthcare

The healthcare field is also one of the fields where breakthroughs are expected by utilizing big data. Current initiatives include DPC (Diagnosis Procedure Combination) and NDB (National DataBase), wearable devices that are expected to become widespread in the future, and healthcare data chains as social models.

DPC and NDB are exceptional examples of big data utilization that have been put into practical use in 2018. DPC is a system in which a medical institution anonymously submits medical expense data to the Ministry of Health, Labor and Welfare, and the Ministry of Health, Labor and Welfare aggregates and announces it.

Since detailed data such as height, weight, disease name and treatment details are accumulated for each hospital, it is thought that it can be used for evaluation and improvement of individual hospital level and community medical levels.

In addition, the national government stores medical data, including DPC and medical receipt information, as NDB. A wearable device is an ICT terminal that is worn and used.

Data such as weight, blood pressure, heart rate, calories burned, etc., are sequentially accumulated and utilized via the terminal. Data is expected to be used in fields such as health management, sports, and medical care. In fact, Kanagawa Prefecture is advocating the construction of a "healthcare ICT system" in which the fields of citizens, companies, government, medical care, research, and long-term care are linked.

Big data use case 4: Marketing

The marketing industry also carries out personalized campaigns by utilizing big data.

For example, by linking online behaviors such as website visit history and purchase history on EC sites with offline purchasing behaviors, a “customer journey analysis” that crosses online and offline has been realized. A customer journey is a journey that compares the process by which a customer recognizes a product, raises interest, and makes a purchase.

Analyzing the customer journey enables you to take the right approach to your customers at the right time. This also makes it possible to analyze big data of customer behavior that can be used for marketing.

Many marketing technologies are for accumulating and analyzing big customer data. One of them is Marketing Automation (MA), which scores (scoring) and visualizes customer behavior to achieve appropriate communication.

The advantage is that it is possible to highlight which customers are promising, even those that cannot be seen alone by the human rule of thumb. Our Spiral® is also a platform that improves marketing and sales productivity based on customer data. Big data analytics in marketing will become more and more taken for granted.

“Big data and cloud computing”

Conclusion

Cloud computing technology provides a compliant framework suitable for big data through ease of use, access to resources, and low cost of resource use in supply and demand, and is a robust device used to process big data. Minimize the use of. Both cloud and big data focus on increasing corporate value while reducing investment costs.

 Cambridge University article about  “Big data and cloud computing” :

Big data and  cloud computing