High-Performance Data Warehousing Explained

April 24, 2015 | By | Reply More

Warehouse

By Jeanneg

Once upon a time data was simply data, unlike what it is today. You have analytical

data, real-time facts, big data, machine information and so on. In the same way even

users have moved into dashboards, reports, analyses, scorecards, sensor facts and the

social media.

This spurt of data growth seems to have burdened various tools like data warehousing

(DW), business intelligence (BI) and data integration (DI). And this pressure demands

that systems have to handle quicker and larger loads of work. A lot of companies utilize

business analysis to draw out more facts, meaning the strain on these tools, are

greater. Fortunately there are other ways out of this situation. The solutions evolved

comprise of a combination of vendor applications as well as optimization techniques

that help in increasing the performance of the company.

In a majority of user companies data warehousing and the like have to tolerate the

stress of performance that unfurl at every complicated layer.

High performance DW tools integrate the vendor performance with user

maximization  

Performance objectives are not easy to accomplish, however a lot of the present

problems are taken care of by the technical innovation of the vendor platforms and

solutions. Today application platforms like massively parallel processing (MPP),

clusters, server virtualization, grids clouds and SaaS can be obtained. In addition

performance improving features like Hadoop, in-database analytics and MapReduce

have also been introduced.

Though vendor tools for higher performance are imperative, user maximization should

play a major part. The more thriving user companies have their standards pre-set, their

style sheets and designs that promote better performance are already in place. Thus

the application of vendor solutions combined with the standards set by the user

company help resolve many of the hitches straight out. In spite of this the user

organization would necessarily have to fine tune and bring about calculated alterations

in the business intelligence products and analytical functions. This is where the

technical expertise of people who work on SQL comes in handy.

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