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The standard of data processing has shifted away from structured transactional data stored in data warehouses as the primary method of collecting data. These traditional data warehouses have been slowly phased out by many organizations moving toward a complete digital data management approach.
In today's hybrid and multicloud world, managing diverse resources scattered across clouds, on-premises, and the edge can be complex. Azure Arc tackles this complexity by offering a centralized management console that unifies your entire environment.
There are various types of data like financial, logs, sensor, audio, video, IoT, and more coming from different sources. These datatypes are semi-structured or unstructured, which makes it difficult for applications to directly ingest the data without pre-processing it. Organizations must rely on custom ETL processes to extract, transform, and load data within the data lake. This process can be time-consuming and prone to errors, making it challenging to efficiently integrate data into the analytics pipeline.
Because data analytics is business critical and resource intensive, using the right infrastructure to run such workloads not only speeds up the analytics processes for end users to quickly derive insight from the data, but it also provides a robust architecture to prevent any unplanned outages. Data analytics infrastructure needs to be powerful, highly available (HA), flexible, and elastic.
When considering modern platforms for data analytics workloads, organizations must carefully assess various options, prioritizing software-defined solutions with integrated compute, storage, and network virtualization capabilities. These platforms should meet or exceed the performance and availability demands of today's most complex applications.
Additionally, data centers grapple with outdated management tools and manual processes, emphasizing the need for new capabilities that balance familiarity for technical staff and robust automation and orchestration. Integrating the services of public cloud providers into a hybrid cloud ecosystem offers flexibility in optimizing the management landscape.
In addition, CPU and storage requirements are important considerations as well, because data analytics workloads are CPU and storage intensive. This document provides insight into CPU and storage options that will meet the requirements of all types of enterprises.