IT Strategy

The Building Blocks Of Big-Data-As-A-Service (BDaaS)

Big Data is outpacing the ability of conventional IT systems to warehouse and process the data that comes in. Fortunately, the capabilities of Cloud computing provide relief to IT departments that might have been overwhelmed by the flow of high-velocity, real-time data and the dynamic variations of structure within that date or possibly the complete lack of structure.

Big Data is a popular term for the increasing quantities of structured and unstructured data in real-time and attempts to extract the greatest possible volume of business intelligence (BI). Like the other functions above, Big Data can also turn to service providers who operate on a subscription model, becoming Big-Data-as-a-Service or BDaaS.

BDaaS should not be confused with the Cloud or any of the layers of technology that are part of it. There are four types of BDaaS, which build services on platforms like Hadoop, infrastructure such as virtualizations, and software functionalities that extract the valuable information hidden within Big Data streams.

The Alphabet Of Services For Big Data

The emergence of Cloud Computing, virtualization, and other innovations in IT sphere is part of an alphabet of defined services that make it possible to forego investments in technology assets. The alternatives they bring are subscription-based services that provide equal or better results quickly and efficiently. The resources of software, infrastructure, and platforms in the Cloud become the services of SaaS, IaaS, and PaaS respectively.

BDaaS combines the tools of this alphabet soup of services, to capture and work with copious amounts of data. The service model provides four distinct ways to integrate a subscription to BDaaS and run it efficiently. Combinations of SaaS, IaaS, and PaaS provide core, performance, feature, and integrated Big Data capabilities for client networks.

The Four BDaaS Types And The Layers They Combine

Core BDaaS – Configurations of services that only employ the platform elements. Platform-as-a-Service or PaaS provides all of the information manipulation tools, such as Hadoop or other distributed database tools. Elastic MapReduce from AWS is a benchmark example of a core BDaaS service.

Performance BDaaS – When customers need an increased level of vertical integration that combines the platform functions of PaaS, they can combine SaaS with the deeper layers of Infrastructure-as-a-service, or IaaS. The Performance type employs infrastructure tools like virtualization or containers, in combination with platform tools that apply structure to and find the insights within the data they receive.

Feature BDaaS – An alternative combination that merges platform and software services to enable easy access to BDaaS for customers who are new to the Big Data arena. This approach takes vertical integration upward. The Software-as-a-service, or SaaS tools provide the surface across which Big Data engages with the users via Web and API interfaces.

Integrated BDaaS – The fully integrated option for BDaaS combines elements from all three services. This level of functionality combines all of the layers from the Feature and Performance BDaaSs into one vertically integrated solution.

You have to keep the blistering pace of change in mind when exploring the BDaaS options. Technology in this space is evolving quickly. For example, the IaaS tools for virtualization such as VM are giving way to containers from IaaS providers such as Docker. The Cloud will continue to provide disruptive changes and new better choices for customers. In all likelihood, the users of applications for Big Data-as-a-service will continue to receive a substantial share of the benefits.

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