Big Data Analytics
Big Data as a Service (BDaaS) refers to an emerging category of analytical data processing services delivered via the cloud. It replaces the complexity, long implementations, and capital expense of on-premises data infrastructure with the ready availability, pay-as-you-go cost model, and elastic scalability of cloud. Big Data as a Service incorporates multiple data processing technologies in order to handle any type of data, analytical workload and SLA, from batch processing to interactive data visualization to real-time streaming data analysis, at an optimal price/performance level. It abstracts away the underlying complexities of data technology stacks, cloud provisioning and ongoing system management so that users can focus on their analytical tools and business outcomes.
Big Data as a Service is an outgrowth of two fundamental IT trends, big data and cloud computing:
- Big data refers to the rapid growth in the variety, volume and velocity of corporate data.
- Cloud computing refers to the migration of software and IT infrastructure from corporate data centers to services delivered via the internet.
The complexity, size, and rapid evolution (see sidebar) of big data fit naturally with the cloud computing paradigm’s ability to scale and simplify. With Big Data as a Service, analytical data processing becomes available “as a service” just like other enterprise IT categories such as CRM (salesforce.com), HR (Workday), and file sharing and back-up (Box).
Big Data as a Service adoption is accelerating as vendors address security, data movement, and other barriers. Additionally, the growing volume of data now generated outside a company’s firewall by mobile, social and IoT applications is shifting the “data gravity” away from the corporate data center and to the cloud.