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Crime Affiliates Essay

Crime in Literature Capitalizing on Elastic Computing

Aside from its potential to integrate and aggregate a host of resources and technologies, Cloud Computing can most prominently benefit the enterprise through the enabling of Elastic Computing.

Elastic Computing is the utilization of the Cloud's nearly limitless scalability to provision resources on demand and at scale without conventional architectural concerns associated with doing so on-premise such as:

Scaling Up and Down -- In physical environments, substantially adding or subtracting resources requires reconfiguring one's architecture to do so -- which is time consuming. Moreover, it is possible that after reconfiguring architecture to scale up, scaling back down again can require even more time than the former process, if doing so is even possible.

Storage -- Storage in on-premise environments is significantly more costly than Cloud-based storage, and was the principle reason that the philosophy of minimizing data within a physical warehouse was derived in order to reduce storage costs.

Network and Bandwidth -- One of the primary issues associated with scaling in physical environments is that doing so requires attaining greater and lesser amounts of bandwidth and accommodating a degree of strain on conventional architecture that is virtually non-existent when utilizing Cloud capabilities.

In comparison, the myriad positives associated with Elastic Computing include:

Increased Speed -- By provisioning resources when one needs them for specific...

"That's a trend that will continue…As we talk to customers, more and more of them have a great deal of interest in doing data analysis in the Cloud. When the data is born in the Cloud it makes it a lot easier."
The Snowflake Elastic Data Warehouse is a Data Warehouse-as-a-Service (DWaaS) offering that utilizes Elastic Computing to specifically provide analytics on what the company terms business data -- meaning both structured transactional data and semi-structured data provided by machines (including sensor or web-based Big Data). Its propensity to do so with the latter data type is all the more notable since it is a fully relational, SQL database that…

Sources used in this document:
Resources -- The degree of specificity for changing and manipulating the various resources to power the database enables users to dedicate particular amounts to computing and storage, which allows them to issue queries while loading the database.

Automation -- Snowflake Elastic Data Warehouse can automate various aspects of data warehouse management (copying data, tuning performances, or spreading data across the system) and assist end users in these processes as they go, instead of involving database administrators or other IT personnel.

Vs. Hadoop

The initial explosion of Big Data largely involved Hadoop as a means of accessing semi-structured and unstructured data, and came to result in organizations attempting to utilize it as an integration hub for their proprietary, on premise data and those that were otherwise. When compared with Hadoop, Snowflake Elastic Data Warehouse is a native relational environment as opposed to the former. This fact accounts for an expedience and ease of integration, in addition to querying and optimization that requires less effort and resources than the latter does. These same factors limit Hadoop's efficacy with transactional data, although Hadoop is open source and is much more cost-effective than a traditional database. Still, it is not a data warehouse.

Final Thoughts
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