Cloud Computing for Big Data Analytics
In the era of digital technologies, the volume of generated information increases exponentially. Сompanies collect huge amounts of data – about the customers, employees, partners, competitors, etc. Most organizations now understand that this data is a source of meaningful insights, and business can get significant value from it. If apply analytics, it is possible to streamline sales and optimize logistics, better understand customers, improve marketing campaigns. This ultimately gives a competitive edge and leads to greater business profitability.
Big Data Growth Trends
According to MicroStrategy, 90% of enterprise analytics and business professionals say data and analytics are key to their organization’s digital transformation initiatives.
Big Data refers to the collection of structured and unstructured data that is huge in volume, complex, and growing exponentially.
In 2015, only 17% of companies around the world used big data in their work. The pioneers in the introduction of big data were IT, banking, and telecommunications businesses. These sectors accumulate the largest amount of data: banks – through transactions, telecoms – through geodata, search engines – through query history.
A couple of years ago, even large companies did not fully understand exactly how to deal with data and what tasks it could help to solve for business. The latest big data stats indicate that more and more organizations realize its huge potential. In the U.S., more than 55% of companies use this technology, in Europe and Asia the demand for big data is about 53%.
Cloud Computing for Big Data projects
Deloitte conducted a survey among more than a thousand managers of U.S. enterprises (staff of 500 or more) and revealed that 63% of respondents are familiar with big data technologies, but do not have the necessary infrastructure to apply them.
Since there is a great potential in the raw data, most companies want to keep it under their control. Besides, corporate and compliance policies may require that data never leave the place where it was created. This means that companies store it for processing, sharing, and analysis in their own data center, on physical servers.
Complex modeling mechanisms for data analysis workloads impose high demands on networks, storage, and servers for real-time data analysis. Maintaining your own data center with dozens of servers implies high costs for supporting physical infrastructure, ensuring its smooth operation, information, and physical security.
This is where the cloud comes in handy. Cloud virtualization eliminates the need for your own infrastructure. This is why some businesses are outsourcing this hassle to the cloud.
The most common models for providing big data analytics solution on clouds are Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS).
IaaS – the provider provides virtual machines with the required computing power to deploy any systems. This approach gives the company maximum flexibility in choosing a platform for big data analysis and control over its configuration.
PaaS – the provider deploys and configures all services in the cloud, the customer only needs to specify the amount of necessary resources. PaaS usually consists of a pre-configured cluster based on open-source data analysis platforms such as Hadoop, Spark with some configured tools.
Benefits of Deploying Big Data Analytics in the Cloud
Scalability. As big data grows exponentially, any system must always be ready for scaling up. You can increase your own storage capacity or run more servers to meet the rapidly growing demands for analytics. But even as you increase the capacity of your local systems, your infrastructure may not be able to maintain them eventually.
Improved analysis. Big data involves manipulating petabytes of data, and the cloud environment allows deploying data-intensive applications for business analytics. Cloud also simplifies collaboration within an organization, allowing more people to access relevant analytics and simplify data sharing.
Security and privacy. These are two major concerns when dealing with enterprise data. The service provider and the client sign a Service Level Agreement (SLA) to gain trust between them. If necessary, the provider also implements an advanced security control level. This enables you to secure big data in the cloud.
Cloud solutions for big data fully meet the above criteria. Cloud flexibility is ideal for analyzing large data sets, so you can increase storage capacity as your information accumulates. But perhaps the biggest advantage of clouds is fast access to data, and thus the increasing speed of processing plus significant savings on server maintenance, installation, and software updates.
Big Data applications in practice
Look at how some of the world’s largest brands made Big Data a part of their business.
HSBC uses Big Data technologies to combat card fraud. With the help of Big Data, the company has increased the efficiency of its security service three times and the detection of fraudulent incidents 10 times.
The economic effect of the implementation of these technologies has exceeded 10 million dollars.
Procter & Gamble uses Big Data to design new products and create global marketing campaigns. P&G has created specialized Business Spheres offices where information is reviewed in real-time. Thus, the company management has the opportunity to instantly check hypotheses and conduct experiments. P&G believes that technology helps in forecasting company activity.
A good example of a global company that collects and analyzes Big Data is Facebook. Every day this social network processes over 2 billion requests received from users. To most fully meet customer preferences and interests, Facebook displays a personalized feed, which includes posts and ads, based on a detailed profile of user activity in the social network and the Internet as a whole.
Another example is Netflix with its data-driven recommendation engine. It saved the company $1 billion per year and influenced about 80% of everything that streamed on the service.