Trending September 2023 # Know The 5 Most Useful Difference Of Cloud Computing Vs Data Analytics # Suggested October 2023 # Top 17 Popular |

Trending September 2023 # Know The 5 Most Useful Difference Of Cloud Computing Vs Data Analytics # Suggested October 2023 # Top 17 Popular

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Difference Between Cloud Computing and Data Analytics

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Hadoop, Data Science, Statistics & others

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 is 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 combines private and public cloud models. Security is an issue here.

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 customer-oriented organizations and at every stage of all transactions. All this data, when modeled correctly, can be analyzed to support effective decision-making in organizations. Hence, the data growth fueled by various 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 measuring and estimating data from big data sources. New analytics trends in real-time streaming data can quickly respond to volatile demands and better quality and value, which pave the way for a digitally-driven organization.

Processing big data from multiple sources needs high-end computing systems and networks that are easily available from cloud service providers. Hence data analytics has become a necessity for organizations to gain valuable insights related to their products or services from different sources of data.

Reduce costs by identifying redundant processes or operations

Make faster and more 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

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. In contrast, data analytics is a technique aggregating 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.

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. Thus the organization can use 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:

Basis for Comparison Cloud Computing Data Analytics


An IT service delivery infrastructure available in different service and deployment models

A framework or a tool for processing data from multiple streams to create analytical models for deriving insights


Provides access to IT resources through the internet

Involves virtualization and abstraction. Characteristics are availability, robustness, flexibility, and scalability to support a variety of IT needs

Analytics involves many techniques, such as algorithms, mathematics, statistics, and mining.

Data from multiple sources are modeled for analysis

Tools can model and manage big data sources

Basis of formation

Cloud service infrastructures deliver dynamic IT services to organizations

IT services are standardized

Ensures IT management costs  are reduced

An outsourced system

Helps organizations to achieve competitiveness

Models data for data-driven discovery and innovation

Integrates data from multiple sources in real-time

Support for effective decision-making based on actual information

Application areas

Applications of clouds are mostly in IT service delivery.

Fulfills a variety of enterprise computing and IT infrastructure requirements

Implemented by almost all sectors (product and service)

Big data modeling and analysis

Business and personal insights

Healthcare – disease diagnosis, predictions

Solutions for retail

Understand consumer behavior


Risk management and fraud detection


Outsourced IT services

IT cost reduction

Innovation and new product or service launch

Reduced time to market

Need for customers to have service availability and robustness.

To verify business process effectiveness

Improve operational efficiency

To monitor organizational performance


In organizations, both Cloud Computing and Data Analytics technology implementations will complement each other towards better performance and value. 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.

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