Making sense of Big Data-As-a-Service
At this point, most people have heard of cloud technologies such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Data as a Service (Daas), etc. Nevertheless, the term Big-Data-as-a-Service (BDaaS) might be a concept that not too many people are familiar with. BDaaS according to the article published by Bernard Marr “Big Data-As-A-Service Is Next Big Thing” refers to the large amount of data being created and stored as well as its analysis and industry applications.
An interesting aspect of BDaaS is the predictive analytics associated to large data sets. It is already known that organisations are making use of these data sets to drive their marketing strategies. Nevertheless, Big Data is wider than that. Industry applications such as product innovation, customer-driven marketing campaigns and sustainability are just a few of the areas that can be exploited from Big Data. There is a vast amount of information posted online every second. The site webpagefx (Internet real time) offers a good insight into the amount of data moved online. It is surprisingly large. So why not make use of it to offer services in return?
One example of an industry using predictive analysis as a way to deliver a service is OpenDNS. A company acquired by CISCO to enhance their security portfolio by adding threat intelligence. This is where predictive analysis becomes a service. Essentially, subscribers from the service redirect all Internet queries to OpenDNS. All Internet traffic is then analysed and a team of security experts develop models to understand patters and abnormal behaviours.
BDaaS combines the power of cloud technologies and applies them to large data sets. Online solutions such as Hadoop enable faster data processing by introducing a distributed computing model. This is particularly important for organisations that require to store and manage large data without facing large upfront costs incurred in creating the infrastructure needed to handle this data. In summary, BDaaS allows organisations to outsource a wide variety of big data queries and analysis to cloud service providers while delivering a “pay what you use” model.
According to an article published on cleverism website. BDaaS is classified into four different delivery models.
· Core BDaaS — Uses infrastructures such as Hadoop, Google’s Map Reduce, Spark or Java-scripts and combines this infrastructure with storage applications such as Amazon’s S3 or Hive.
· Performance BDaaS — Uses basic infrastructure while making use of existing software and hardware services in order to optimise performance. Example (Altiscale)
· Feature BDaaS — Computing and storage are kept independent from the service provider and can be fully scalable. Example (Hadoop ecosystem refined with Amazon’s or Google’s Iaas Software)
· Integrated BDaaS — This type is not currently offered by any service provided. However, if offered it would be expected to merge Performance and Feature BDaaS to allow maximum performance while supporting business owners.
Making sense of Big Data-As-a-Service is the last from a series of four that explore the definition of Big Data-As-a-Service and the industry applications derived from this technology. I would appreciate your comments and personal perception on the subject.
Some questions to consider:
Is this model applicable to all organisations?
Which organisations would benefit the most? Small/Medium or Large organisations?
Would you consider using this service in your organisation? If so, how would you apply it?