The real value of Big Data Platforms as a Service is the ability to quickly scale-up big data projects without the upfront CapEx required for an on-premise deployment. Additionally, organizations can scale down fast and pay only for the storage and compute resources they use.
Big Data PaaS offerings reduce the need for organizations to hire and/or train big data staff, a challenging task considering a lack of skilled big data practitioners at this time. Rather, the service providers are responsible for deploying, managing and scaling installations.
Big Data PaaS may be a good potential starting point for organizations looking to tap into the power of big data analytics but are not prepared to commit to a full-scale, production level deployment at this time.
We are in the pre-industrial age of data analytical platforms and there are many different types of Big Data PaaS offerings. The following is a partial list:
- Google BigQuery
- Microsoft Azure Hadoop
- Google Prediction API
- Mortar Data
- Placed Analytics
- Spring for Hadoop