Although Software as a Service had big usability, expect for few usages, SaaS has been restricted and corporates are in favour of on-premise. Big Data as a Service or BDaaS, is as if combination of SaaS, PaaS and DaaS. Self Hosting Big Data platform is time consuming and costly. Businesses have cloud-based IT spending of about 15% now. The forecasted value of the BDaaS market is higher.
Big Data as a Service (BDaaS) Basics
Apache Hadoop and Apache Spark has to some extent has been standard in the field of Big Data. In our previous tutorials, we talked about how to use virtualised hardware and free software to analyze data. But installing Apache Hadoop tutorial itself demands investing time. It is obvious that, using Apache Hadoop for server log analysis is not exactly easy, Unlike installing softwares for web hosting, most Big Data still involves time to be spent on configuration. When a developer of company launches a testing initiative, this point is likely to be big barrier. Virtualizing analytics has definite advantages. Additionally there is question of server security.
It is normal and obvious to watch the DBaaS platform to promote their platforms ensuring enterprise-level security, performance as well as scalability combined with other advantages for their target market. BDaaS providers usually remains responsible for data protection – if the data is stored on their servers, they are responsible for it. They usually claim that PaaS focusing only on Hadoop and combing IaaS, PaaS, SaaS complete vertical integration for features and performance.
Not all data we need analyse are sensitive or business critical. For log analysis with big data software, it is more practical to use a cheap or free BDaaS service. Seriously using BDaaS demands assessment of own need with questionnaires around infrastructure components, administration, need of data scientists, possibility to integrate, visualize data with existing systems of a company or individual.
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