Businesses today are powered by Big Data mined from every available source and stored on the cloud to ensure agility, security and strategic decision making. Indeed, Big Data and Cloud Computing are a potent combination that has changed how businesses operate. Yet, the relationship between data and computing is often misunderstood.
What is Big Data?
Big Data is a term that refers to large volumes of hard-to-manage data that inundate businesses on a day-to-day basis. It also refers to the field that deals with ways to analyze and systematically extract information or just deal with data sets that are too large or complex to be dealt with by traditional data-processing software.
The challenges to big data analysis include data capture, data storage, search, sharing, transfer, visualization, querying, updating, privacy, and data source. The complexity of big data makes it impossible to process using traditional methods. The concept of big data gained momentum in the early 2000s when Dough Laney defined big data as the three V’s- Volume, Velocity, and Variety.
Organizations collect data from a variety of sources and storing all of those data would have been too costly if not for new and cheaper alternatives like data lakes, Hadoop and the cloud.
Internet of things warrants timely processing of data streams traveling at unprecedented speeds. These torrents of data need to be dealt with at near-real-time speeds.
Data can be of many types – structured numeric data in traditional databases to unstructured text documents, videos, audios, financial transactions etc.
Big Data in the present context
Big Data is synonymous with predictive analytics, user behavior analytics, or other advanced data analytics methods that extract value from big data. We rarely refer to a particular size of data set when big data is mentioned. The relevant characteristic of this new data ecosystem is to find new correlations to spot business trends, prevent diseases, combat crime and more.
The advent of widespread technology, including mobile phones, has tremendously increased the size and number of available data sets. Internet of Things (IoT) devices, aerial sensing, software logs, cameras, microphones, Radio Frequency Identification (RFID) and wireless sensor networks are just a few of the many cheap and numerous information sensing devices that add to the data pile.
Applications of Big Data
The increasing use of data-intensive technologies by developed economies has spurred the demand for information management specialists and data analysts across the globe. Between 1 billion and 2 billion people are accessing the internet and there are 4.6 billion mobile-phone subscriptions worldwide.
- Government Use of Big Data
Big data use in government processes allows efficiencies in cost, productivity and innovation. But it does have its flaws; for instance, the National Security Administration (NSA) monitors internet activities constantly to search for patterns that have the potential to lead to illegal activities. Privacy is an aspect that is still being debated in the context of big data.
- International Development
Big data offers cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime, security, natural disasters and resource management. User-generated data is also an avenue for the unheard to be heard. However, technological infrastructure and economic and human resource scarcities remain challenges to developing regions. These scarcities can potentially exacerbate existing big data concerns like privacy, imperfect methodology, and interoperability.
Big data has been significant in the fight against poverty by providing satellite imagery and machine learning for poverty prediction. For instance, the labor market and the digital economy were studied using digital trace data in Latin America.
Big data analytics plays a significant role in healthcare through:
- Personalized medicine
- Prescriptive analytics
- Clinical risk intervention
- Predictive analytics
- Waste and care variability reduction
- Automated external and internal reporting of patient data
- Standardized medical terms and patient registries
Human inspection is impossible at the volumes of big data scales, and intelligent tools for accuracy, believability control and handling of information are required. These are conducted with the help of specific analytic tools. Data-driven analysis has helped in exploratory biomedical research as it can move forward faster than hypothesis-driven research. Computer-aided diagnostics is another aspect of healthcare that depends heavily on big data.
By providing actionable insights into millions of individuals through big data, a message or content that aligns with the consumer’s mindset can be conveyed. Consumers are tapped into with the help of targeted content that reaches people at optimal times in optimal locations. The current advertising ecosystem is a good example of this.
Internet of Things (IoT)
The media industry, companies, and governments use the data extracted from IoT devices to accurately target their audience and increase media efficiency. The sensory data gathered from the devices are used in medical, manufacturing and transportation contexts. Ideally, the data from devices would help track and count everything and tremendously reduce waste, loss and cost.
Big data has helped business operations as a tool to streamline the collection and distribution of Information Technology and improve employee efficiency. Deep computing and machine intelligence help IT departments predict and prevent issues.
Machine Learning and Artificial Intelligence
Big data is a crucial component used in the training of complex models and facilitating Artificial Intelligence.
What is Cloud Computing?
It is the on-demand availability of computer system resources like data storage and computing power without direct active management by the user. Large clouds often have their functions distributed over multiple locations, with each location acting as a data center. Reliance on sharing resources to achieve coherence is a characteristic feature of cloud computing.
Cloud storage makes it possible to save files on a remote database instead of a proprietary hard drive or local storage device.
Types of Cloud Computing Services
Cloud computing services provide users with a series of functions including:
- Storage, backup, and data retrieval
- Creation and testing of apps
- Data analysis
- Video and Audio Streaming
- Software on demand
Benefits of Cloud Computing
- Increased productivity, reduced costs, speed, efficiency, performance, security, and portability are just a few of the reasons why Cloud Computing has seen an increase in popularity lately. The heavy lifting involved in processing data is done by the remote system instead of the device that a person carries to work.
- Being a fairly new service, cloud computing is used widely in the business space by big corporations, small businesses, non-profits, government agencies and even individual consumers.
- Before cloud computing, businesses were required to purchase, construct and maintain costly information management infrastructure. Now companies can swap costly server centers and IT departments with fast internet connections over which employees can interact with the cloud to complete their tasks. This has resulted in tremendous cost and time savings.
- Reliance on tangible software upgrade methods like discs and flash drives is replaced by faster methods of internet upgrades. It also helps individuals save storage space on their desktops or laptops through customer data cloud services.
Cloud Computing Concerns
- Security has always been a natural concern when it comes to sensitive medical records and financial information. Encryption protects vital information, but the loss of the encryption key would result in data loss. The issue of security in cloud computing is an ongoing issue, with regulations forcing service providers to shore up their security and compliance measures.
- Then there is the issue of server damage. For instance, a server company can fall victim to natural disasters, internal bugs, power outages and more. A blackout in one part of the world could affect the users accessing that data from another country.
Big Data on The Cloud
Big data before the cloud strained the financial and intellectual capital of even the largest businesses by the sheer amount of computing resources and software services needed to support the effort. With the advent of cloud computing technology that provides almost limitless computing resources and services, big data initiatives are now possible for any business, big or small.
Enterprise Case Studies for Big Data on the Cloud
- Netflix on AWS
Being one of the largest media and technology enterprises in the world, Netflix stores billions of data sets in its systems concerning audio-visual data, consumer metrics and recommendation engines. AWS gave the company a solution that would allow it to store, manage, and optimize viewers’ data and offered a platform that would enable quicker and more efficient collaboration on projects. It enabled Netflix to discover and respond to issues in real-time, ensuring high availability and a great customer experience.
- 2. mLogica on SAP HANA Cloud
mLogica, a technology and product consulting firm, wanted to move to the cloud to better support its customer’s big data storage and analytics needs. SAP HANA Cloud enabled the move from on-premises infrastructure to a more scalable cloud structure. It helped them to manage growing pools of data from multiple client accounts, improve slow upload speeds for customers, move to the cloud to avoid maintenance of on-premises infrastructure and integrate the company’s existing big data analytics platform into the cloud.
Advantages of Big Data in the cloud
The physical constraints of a typical business data center significantly throttle its ability to conduct business. A public cloud negates the space, power, cooling and budget requirements of a big data infrastructure by managing hundreds of thousands of servers spread across a fleet of global data centers. It also translates to savings in time as the cloud infrastructure is already built and ready to go.
Businesses can choose to employ the required number of servers according to the budget and task at hand, and then later release them when the task is complete.
Hardware, facilities, power and maintenance are just a few of the expenses for building and maintaining a business data center. The prohibitive cost and wastage are removed through a flexible rental model where resources and services are available on-demand and follow a pay-per-use model.
The significant global footprint of cloud services enables resources and services to be deployed in most global regions. The data and processing activity can take place proximally to the area where the big data task is located instead of moving that data to another region.
Replication of cloud data across its servers is standard practice by service providers to ensure high availability in storage resources and maintain cloud data resilience.
Cons of Big Data in Cloud Computing and Ways to Overcome them
Even though the value of big data in cloud computing is tremendous, it’s not without its pitfalls. Businesses need to consider potential drawbacks before adopting a public cloud or a third-party big data service.
- Network Dependence
The effect of outages is a significant concern in any big data use of the cloud. Cloud use requires complete network connectivity from the LAN, through the internet, to the cloud provider’s network. An outage at any point in this chain could result in increased latency at the best of times and complete cloud inaccessibility at worst. Data replication and usage of reliable networks and cloud services can reduce this risk considerably.
- Storage Cost
Data storage can be a substantial long-term cost for big data projects on the cloud. The loading of data into the cloud is time-consuming and the storage instances incur a regular fee. Data migration, which refers to the transfer of data between servers, may incur additional fees and loss of time. Hence, comprehensive data retention and deletion policies must be employed by businesses to prevent retaining unnecessary data and deal with time-sensitive big data sets.
The data involved in big data projects can contain proprietary or personally identifiable data that is subject to data protection laws and other industry or government-driven regulations. Security of such data is paramount. Cloud users must take the steps needed to maintain security in cloud storage and computing through adequate authentication and authorization, encryption and copious logging of access and usage of data.
- Lack of Standardization
As there is no single way to implement and operate a big data deployment in the cloud, there is a potential for poor performance and exposure to security risks. Documentation of big data architecture, along with policies and procedures of use, should be prioritized by business users to sidestep potential problems. This documentation can become a foundation for future optimizations and improvements.
Choosing the right customer data cloud provider
Even though the underlying hardware gets the spotlight, it’s the analytical tools that make big data analytics possible. Providers such as SAP can arrange for support and consulting to help businesses optimize their big data projects so that they won’t need to start their initiatives from scratch.