How Cloud Analytics Differ from Regular Data Analytics

Essentially, cloud analytics is a form of data analytics where either data or processing or both are hosted in the cloud. Unlike conventional data analytics, cloud analytics offer significant advantages in terms of flexibility, agility, and cost-optimization.

Comparison Table: Regular Data vs. Big Data

Aspect Regular Data Big Data Volumes Limited Into Petabyte Volumes Batch Velocities Slower Real-time Velocities Structures Varieties Homogeneous Multis structured Varieties Tools Practices Dominant Data Platforms Data ware house systems OLTP/EDW built on Relational DBMS Disaster Recovery Memory management Optimization Backup Recovery Security Governance Auditing Replication Virtualization Archiving Data Computation Pig Latin Scripts Regular Expressions

What is Cloud Analytics?

Cloud analytics is a subset of data analytics where analysis is performed on the data present in a public or private cloud. Any business intelligence process carried out in collaboration with a cloud service provider can be termed as cloud analytics. Cloud service providers offer analytics solutions through their cloud models, typically as SaaS (Software as a Service) or PaaS (Platform as a Service), where users can create their proprietary analytics software to run on the cloud storage infrastructure.

Advantages of Cloud Analytics

Fast and Affordable: Cloud analytics leverages scalable resources, offering fast and cost-effective solutions. Security Intelligence: Enhanced security measures and intelligent threat detection are critical features in cloud analytics. Self-Reliant and Scalable: Cloud analytics allows for self-reliant operations and scalability, adapting to the growing data volumes. Predictable Costs: Ditching upfront infrastructure costs for subscription-based models offers predictable and controllable expense management.

Comparing Cloud Analytics and Traditional Analytics

The comparison between cloud analytics and regular data analytics highlights significant differences in terms of volume, velocity, and variety.

Volume: Traditional data analytics deals with limited volumes, whereas cloud analytics can handle multi-petabyte volumes. Velocity: Cloud analytics emphasizes real-time data velocity, whereas regular data analytics operates on batch processing. Variety: Cloud analytics supports complex, multi-structured data varieties, unlike the homogenous data in traditional analytics.

Conclusion

Cloud analytics is becoming increasingly important in the Business Intelligence domain, especially with the growing volumes of data in the cloud. By leveraging cloud services, businesses can achieve cost-effective, secure, and scalable solutions for their analytics needs.