CLOUD CUSTOMER ARCHITECTURE FOR BIG DATA AND ANALYTICS
Using analytics reveals patterns, trends and associations in data that help an organization understand the behavior of the people and systems that drive its operation. Big data technology increases the amount and variety of data that can be processed by analytics, providing a foundation for visualizations and insights that can significantly improve business operations.
Cloud Customer Architecture for Big Data and Analytics considers how harnessing cloud architectures can further change the economics and development lifecycle of these capabilities. It describes vendor neutral best practices for hosting big data and analytics solutions using cloud computing. The paper describes the architectural elements and cloud components needed to build out big data and analytics solutions.
The primary drivers for deploying analytics solutions on cloud include:
- Low upfront cost of infrastructure and a reduction in the skills needed to get started.
- Elastic data and processing resources that grow and shrink with demand, reducing the need to maintain capacity for the maximum workload.
- Mitigation against limited internal capability for meeting information governance, compliance and security requirements.
- Applying more processing resources to existing data sources.
- Building solutions faster because it enables try and buy, rapid prototyping and shorter procurement processes.