Difference Between Cloud Computing and Data Analytics
Cloud computing refers to the delivery of IT as a service from data centers. The word cloud is used as a metaphor to represent the internet due to its vast resource repository and information to suit different user needs. Resources in the cloud include servers, bandwidth, network, storage, etc. along with software and OS platforms. Cloud makes IT resources available as a utility, which is similar to the power utility we have in our homes. The concept of cloud computing is derived from computing architectures such as grid computing and virtualization in combination to provide utility service computing.
Cloud involves centralization of resources (hardware and software) which are made available as a service. Cloud services are provided by a cloud service provider (CSP). Some examples of CSPs are Amazon Web services, Microsoft Azure, Google, IBM, etc. Consumers/Users are billed based on each resource consumed and for the resource availed over time. Clouds have many advantages which make it the most ideal option for organizations, big or small. Some of the characteristics of clouds include,
- Scalability, availability, reliability, and robustness
- Cost-effective and flexible
- Enhanced business value and agility
- Improved operational efficiency
Cloud services are classified as service models and deployment models. The service models are:
- Infrastructure-as-a-Service (IaaS)
- Platform-as-a-Service (PaaS)
- Software-as-a-Service (SaaS)
Cloud deployment models are:
- Private clouds: This model is an in-house or an outsourced privately-owned data center infrastructure with good levels of security and is expensive.
- Public clouds: This is a cost-effective model and mostly available for free on the internet. Examples include Google Gmail, Google Drive, etc. Here the data is not fully secure.
- Hybrid clouds: This model is a combination of private and public cloud models. Security is an issue here.
All cloud resources and models are made available through the internet. Access to the resource is possible with any standard browser software or with any device that connects to the internet.
Due to the emergence of new technologies, we are witnessing a big data deluge due to substantial changes made in the interactions in business to consumer, or business to business and between organizations. New data is generated continuously, especially in organizations that are customer-oriented and at every stage in all transactions. All this data when modeled correctly can be analyzed to support effective decision making in organizations. Hence, the growth of data-fueled by a variety of devices and the internet has the potential for unprecedented opportunities.
Data analytics can be understood as analytical modeling or preparing data for accurate quantitative analysis. Data analytics is required for extracting insightful information to drive continuous improvements and to understand trends and business performance. Thus analytics is understood as measurement and estimation of data from big data sources. New analytics trends in real-time streaming data have the ability to quickly respond to volatile demands, better quality, and value which pave the way for a digitally-driven organization.
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Processing big data from multiple sources need high-end computing systems and networks which are easily available from cloud service providers. Data analytics can be used in the cloud because it ensures high levels of efficiency along with computing and storage capabilities to handle large volumes of big data on the internet. Hence data analytics has become a necessity for organizations to gain valuable insights related to their products or services from different sources of data.
Data analytics is important for organizations because it helps to,
- Reduce costs by identifying redundant processes or operations
- Understand customer preferences, to provide customized products or service, leading to better competitiveness
- Make faster and effective decisions based on current information
Head-to-Head Comparison Cloud Computing and Data Analytics (Infographics)
Below is the Top 5 Comparison Between Cloud Computing vs Data Analytics
Key Differences Between Cloud Computing vs Data Analytics
Below are the key differences mentioned:
- Both cloud computing and data analytics platforms offer cost reduction and efficiency for organizations towards achieving business agility. However, cloud computing is a technology or infrastructure to provide continuous and dynamic IT services whereas data analytics is a technique that aggregates data from multiple sources for data modeling and data preparation for deeper analysis.
- Clouds provide scalable compute, storage and network bandwidth capacities for big data applications. On the other hand, data analytics need IT infrastructures to process and model incoming data streams at high speed. Thus clouds and data analytics can go together.
- Clouds services provide solutions for all types of data-intensive processes. This is opposed to analytics which performs deep insights and discovery towards improving organizational performance.
- Cloud infrastructures can integrate well with existing systems and hence they can link different departments and data across the organization to build a centralized data model. Data analytics is easily performed in centralized data compared to a distributed data store.
- Cloud services are accessed through the internet, thus the organization can make use of developed analytical models to collaborate with other organizations, monitor markets and gain competitiveness.
Cloud Computing vs Data Analytics Comparision Table
The differences between cloud computing vs data analytics are explained in the points presented below:
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Conclusion
Therefore, in summary, it may be noted that cloud computing services and most ideal for data analytics applications. This is because, with rapid growth in big data, organizations need an appropriate and adequate environment for managing big data processes which are enabled by cloud services. In organizations, both Cloud Computing and Data Analytics technology implementations will complement each other towards better performance and value.
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