Is The Cloud Ready For Data Warehousing?Infinite capacity, fast deployments, and high availability – at trivial cost. What’s not to like about public clouds? Even setting data privacy and security aside, for data warehousing, likeability depends on database workloads.

True, Web and online start-ups have shown us that the cloud can support transaction processing, with its relatively simple and predictable access patterns.

But for the complex compute- and read-intensive characteristics of business intelligence (BI) and analytics workloads, success with public clouds is problematic. If the history of the commoditization of electricity generation and transmission teaches us anything, it’s that we’re at least five years away the mature, standardized public cloud architecture needed to support BI and analytics. So for the next five years, IT will be discussing private clouds as the promising alternative.

In a recent white paper from Third Nature, research analyst and former CTO Mark Madsen describes the experiences of several organizations that have moved from traditional server-based data warehousing to a private cloud. The benefits of applying the private cloud model to standard approaches for BI analytics are compelling.

Enterprise-wide control: From system to platform – The data warehouse is no longer constrained as a passive repository and reporting system that often left BI and analytics siloed. The cloud-based data warehouse allows IT to support separate environments for different groups – all integrated and managed within a single platform.

Cost: From product to service – Planning data warehouse infrastructure no longer centers on capacity planning and up-front payments. The cloud pay-for-use model allows companies to shift the mix of money from capital expenses to operating expenses, which can be pushed to the business departments and lead to faster management approval of BI projects.

Performance: From managing for peaks to self-service provisioning – Environments are no longer sized for expected peak workloads – and adjusted quarterly at best. On-demand and automatic resource provisioning accommodate the unplanned, real-time demand typical of BI and analytics projects.

Agility: From long lead times to on-demand capacity – Large projects are no longer the only above-the-line projects. On-demand resources allow IT to quickly develop, test, and deploy even small BI and analytic projects as needed.

According to Madsen, the private cloud works as an environment for BI and analytics workloads, delivering many of the benefits of public clouds without some of the problems. Download “Cloud Computing Models for Data Warehousing” for an in-depth discussion of this topic.