Challenges and Outcomes Using Big Data as a Service

Big Data as a Service (BDaaS) encompasses software, data warehousing, infrastructure, and platform service models to deliver advanced analysis of large data sets, generally through a cloud-based network. It is a solution-based system designed to provide organizations with wide-ranging capabilities to gain insights from data. With a rapidly growing amount of data, businesses require advanced technologies to harness the power of data science effectively. These convergent technological trends will significantly redefine business applications for both e-business and more traditional sectors such as manufacturing or logistics. As enterprises acquire the opportunity to store and analyze gigantic amounts of data, ranging from detailed customer behavior traces to demographic shifts of clientele in emerging markets, they play a significant role in creating novel business opportunities. Further, it helps them to disparate data to drive insights through which the businesses can make efficient decisions and create a competitive advantage. Practically, big data services are most important for business applications, not just because the data is big but also because its potential or impact is tremendous. Despite the advantages, the major challenge in dealing with big data applications is that it requires more innovative technological solutions that replace traditional databases with more scalable architectures. This is where big data as a service (BDaaS) can make a difference for business applications. BDaaS has been commercialized and implemented in many business platforms. The cloud infrastructure required to use BDaaS is provided by both Microsoft Azure and Google BigQuery, which are both data services. The cloud data lake, data warehousing, and data sharing are located on top of the data services layer. Their purpose is to offer the analytics and sharing bridges that lead to end-user analytics. …