Few trends have had a bigger impact on the data platforms landscape than the emergence of cloud computing. The adoption of cloud computing infrastructure as an alternative to on-premises datacenters has resulted in significant workloads being migrated to the cloud, displacing traditional server and storage vendors. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research currently use cloud computing products for analytics and data, and a further one-quarter plan to do so. In addition to deploying data workloads on cloud infrastructure, many organizations have also adopted cloud data and analytics services offered by the same cloud providers, displacing traditional data platform vendors. Organizations now have greater choice in relation to potential products and providers for data and analytics workloads, but also need to think about integrating services offered by cloud providers with established technology and processes. Having pioneered the concept, Amazon Web Services has arguably benefitted more than most from adoption of cloud computing, and is also in the process of expanding and adjusting its portfolio to alleviate challenges and encourage even greater adoption.
Data lakes have enormous potential as a source of business intelligence. However, many early adopters of data lakes have found that simply storing large amounts of data in a data lake environment is not enough to generate business intelligence from that data. Similarly, lakes and reservoirs have enormous potential as sources of energy. However, simply storing large amounts of water in a lake is not enough to generate energy from that water. A hydroelectric power station is required to harness and unleash the power-generating potential of a lake or reservoir, utilizing a combination of turbines, generators and transformers to convert the energy of the flowing water into electricity. A hydroanalytic data platform, the data equivalent of a hydroelectric power station, is required to harness and unleash the intelligence-generating potential of a data lake.
Enterprises looking to adopt cloud-based data processing and analytics face a disorienting array of data storage, data processing, data management and analytics offerings. Departmental autonomy, shadow IT, mergers and acquisitions, and strategic choices mean that most enterprises now have the need to manage data across multiple locations, while each of the major cloud providers and data and analytics vendors has a portfolio of offerings that may or may not be available in any given location. As such, the ability to manage and process data across multiple clouds and data centers is a growing concern for large and small enterprises alike. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research study are using cloud computing for analytics and data, of which 42% are currently using more than one cloud provider.