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Data virtualization has become popular among large enterprises because unstructured and semi-structured data is common and using this data is challenging. Marketing leaders across industries expect the data to be available for easy consumption to help speed up their decision-making process.
Data analytics is the process of analyzing raw, structured, and unstructured data to identify trends and answer questions. This type of analysis allows a business to interpret and communicate meaningful data patterns. There are several approaches to data analytics, including the following:
Organizations can apply analytics to business data to describe, predict, and improve business processes and outcomes.
Organizations rely on data analytics to make better business decisions. Using the right infrastructure to run such workloads not only speeds up the analytics process, but it also provides a robust architecture to prevent any unplanned outages. Data analytics infrastructure needs to be powerful, highly available (HA), and flexible—as does any infrastructure designed for business-critical applications.
Data analytic workloads can be CPU intensive and selecting the optimal CPUs for the data analytic servers can be challenging, time consuming, and expensive. There are three common CPU components that should be considered when choosing a CPU:
Storage is another important factor for data analytic workloads. The most common storage for unstructured and semi-structured data is S3-compatible object storage. There are several object storage options available on the market, but not all options are the same. Procuring the right object storage for data analytic workloads can be complex, and time consuming.