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As enterprises look to reduce costs and increase data availability, technologies such as data virtualization are used to meet these organizations' data needs. Data analytics is the process of analyzing raw, structured, and unstructured data to answer business questions and identify trends.
Data virtualization allows enterprises to seek flexible and cost-effective storage options for structured, unstructured, and semi-structured data like ECS, which uses Simple Storage Services (S3) to streamline data pipelines. The PolyBase feature enables new business opportunities for Microsoft SQL Server through data virtualization. Data virtualization is sought after by large enterprises because it allows them to face the challenges of working with unstructured and semi-structured data while minimizing costs.
Data accessibility is an expectation among enterprises, and data virtualization facilitates the consumption of data to enable quick data driven decisions. Organizations are adopting data virtualization to virtually integrate different types of data for data mining, machine learning, artificial intelligence, and data analytics.
Organizations rely on data analytics to gain descriptive, predictive, prescriptive, and diagnostic based insights so that they can make meaningful changes to the enterprise’s operations. Data virtualization allows for the appropriate infrastructure to be put in place to provide high availability for these business-critical data analytic services.
Storage and CPU requirements are important considerations because data analytic workloads are frequently CPU and storage intensive. This document seeks to provide insight into CPU and storage options that will meet the needs of all types of enterprises.