Big data as a service is the provision of data platforms and tools by a cloud provider to assist enterprises in processing, managing, and analyzing massive data sets so they may produce insights to improve company operations and achieve a competitive advantage.
Big data as a service (BDaaS) aims to free up organizational resources by leveraging data management systems and IT expertise from external service providers rather than building on-premises systems and hiring internal staff to perform these tasks. This is because many companies regularly generate large amounts of structured, unstructured, and semi-structured data. Big data as a service can be hard-working systems and software running in the cloud or managed service contracts hosted and managed by cloud service providers.
BDaaS is a type of cloud computing like Software as a Service, Platform as a Service, or Infrastructure as a Service. In addition to the core data processing framework and related tools, Big Data as a Service relies on cloud storage to manage datasets and provide access to user organizations.
The AWS, Google, and Microsoft platforms can now be installed in users' data centers and other on-premises facilities, even if the public cloud remains the main emphasis of big data as a service industry. This is enabled by the addition of support for running big data services on each vendor's hybrid cloud architecture, in that order: AWS Outposts, Google Anthos, and Azure Stack. In their big data settings, organizations can employ these technologies to build private clouds or mix public cloud and on-premises systems.
All three manufacturers have also integrated Kubernetes services into their BDaaS platforms, enabling businesses to build containerized big data apps using the well-known container management architecture. This can help enterprises simplify deployments, expedite infrastructure management, and maximize the usage of resources. Big data as a service can refer to a contract for a managed service hosted and operated by a cloud vendor, or it can refer to diligent systems and software that is cloud-driven. Furthermore, AWS, Google, and other BDaaS vendors are emphasizing Spark and other technologies over Hadoop, which was previously at the heart of their offerings and the big data ecosystem. This reflects a broader decline in Hadoop's standing in comparison to Spark as a batch processing engine, even though HDFS and Hadoop's YARN cluster resource management software are still widely used.
According to Grand View Research, the large enterprise segment will gain the fastest market share in 2018 due to the rapid growth in the number of data sets combined with various processing solutions such as data warehousing, data transformation, data migration, and data hubs. According to the company, the increasing involvement of banks and financial institutions in designing, developing, and supporting ETL for large datasets is expected to drive the growth of the market.
Again, not all BDaaS solutions are created equal (e.g., different types and differences regarding elements like security and data governance, advanced analytical capabilities, visualization, and an end-to-end approach), and as Markets Insights Reports notes, the enhanced flexibility and customization to study and alter data, thus giving improved data management and accessibility to complicated data analysis, makes Big Data as a Service a feasible solution for large businesses.
In addition to offering services at the infrastructure, platform, and software levels, big data as a service also offers various levels of services. The utilization of these services and their incorporation into other systems are simple. Big Data-as-a-Service has the attributes of being cross-language, cross-platform, and cross-firewall by encapsulating intricate details. This allows for the construction of dynamic big data services and presents the excellent potential to develop new business values. Big Data-as-a-Service is being looked at to offer common functionality for big data management and analysis. Users will be able to access the service-generated big data and the outcomes of big data analytics via APIs.
Interested in knowing more about Big Data Analytics? We can help!