In this short article, I want to briefly introduce the business value, architecture, use cases, deployment, and service model of Big Data as a Service (BDaaS) which is a novel and rapidly growing Cloud-based service.
The service is offered to support big data and analytics solutions in a cost-effective manner. This service can be considered as a type of outsourcing model for the deployment of big data and analytics projects rapidly and inexpensively. It is considered inexpensive because we can use the service without upfront investment for costly underlying infrastructure hosting the big data and analytics solutions. Underlying infrastructure costs can include computing servers, storage, network, appliances, racks, hosting facilities, and more importantly infrastructure management and support costs.
The service presents multiple use cases and solution goals. The primary use case for BDaaS is a supply of data management and analytics tools performing the actual analysis and providing required reports to the relevant stakeholders in desired formats. Some DBaaS service providers can also offer additional cost-effective services such as advisory and consulting services to complement their consumption-based services.
BDaaS can be an excellent opportunity for small business or start-up companies, and even for large business organizations with a limited budget and resources allocated for big data and analytics solutions. The use of this service can increase competitiveness, innovation, and revenues for consumer business organizations.
Let’s take a deep breath and re-focus our attention before talking about the architectural and deployment details 🙂
From an architectural perspective, BDaaS is based on SOA (Service Oriented Architecture) combined with virtualised storage, scalability, event-driven processing, and analytics tools. The delivery mode of BDaaS is based on Cloud consumption model. The critical consideration for the big data solution architects is to be aware of the big data and analytics requirements and which service model can fit into the requirements of the solution in the most cost-effective way.
For the deployment practitioners’ consideration, there are different deployment models for BDaaS. Some BDaaS providers can provide, core, performance-based, feature-based, and integrated services. The terms and conditions may be different for each service provider hence the deployment practitioners need to understand T&C and negotiate the proposals accordingly.
The primary value propositions for BDaaS are rapid deployment capability, capacity extension, scalability on-demand, and established Quality of Services for network speed and Service Level Agreements. The key business outcomes are the agility and cost-effectiveness with guaranteed service levels without an investment of funds on massive internal IT costs.
BDaaS is a relatively new service however it is proliferating globally. We can find many Cloud service organisations providing big data and analytics based on self-service in their data platforms. Some established and popular BDaaS providers are Amazon Web Services, Google Cloud Dataproc, Salesforce Wave Analytics, IBM BigInsights on Cloud, Microsoft Azure HDInsight, and Qubole Data Service.
BDaaS is well received and considered a valuable proposition by consumers. It also poses profitable business for the service providers hence the number of service providers is globally increasing. In addition, more and more granular, customised services, and bespoke solutions are being offered by emerging service providers. I informed and highlighted BDaaS in this article because it can be a valuable business proposition for data-driven investors, start-ups, and big data entrepreneurs.