How To Reduce The Cost Of Cloud Storage Through Business Intelligence And Real Data Analysis

2 mins read

With all the hype and buzz that surrounds cloud computing, applications in current public cloud services represent a small portion of the total information technology spending. It will remain the case unless mission-critical applications and the mainstream IT community that takes up large chunks of the capital can go over make the switch to public or private cloud.

One of the biggest worries about adopting cloud computing is the high cost of public and private cloud storage. Web-related cloud applications have the ability to store a few hundred megabytes of data while mission-critical apps store terabytes of information on prevailing prices which may be too much for any user.

The good thing is that there are two strategies in addressing the cost of cloud storage – query-distributed access to data and data abstraction.

Reducing The Cost of Business Intelligence And Analytics With data Abstraction

Business intelligence and analytics are some of the most essential and promising applications for the cloud. The said applications, are clustered in time around most IT decisions and spread over a variety of planners and decision-makers. That is why these applications are ideal for cloud applications. Creating big data in real-time without becoming too large is an exercise in the first of the two cost management approaches.

Data abstraction is a type of mechanism that creates one or more database summaries from raw enterprise or company information which is small enough to be saved and stored in the cloud economically. Making data abstraction is one way of having an effective cost management technique that requires analysis of how and what you are analyzing. Most business intelligence is not looking for detail but market trends.

In most industries, there are variables that are important in gaining the best information. By creating summary databases on the variables, you will be able to cut the cost of speeding up access without having a negative impact on the analytical work. If a specific variable combination is identified as important to data gathering, you can go back and extract details for that combination from any un-summarized data if needed. Abstraction-based analytics will become a cloud application. It will also detail analysis and processes for the cloud.

Using Query-Distributed Acess For Any Unstructured Data

The data abstraction works well with applications that analyze transactional data that are structured around a small number of essential variables. Data abstraction however will not work well with big data in its unstructured and traditional form because any unstructured data can’t be easily abstracted. Some enterprises and organizations have had some success with creating databases that show the rate of appearance of any specific combinations of words. It also presumes that the important words/combination can be identified in advance.

Data processing tasks have three known components – the actual data processing, database management access (to locate the data) and storage access (to get information from mass storage devices). If large amounts of data cannot be saved and placed in the cloud because of the cost. The data cannot be pulled into the cloud record-by-record also thus the best approach to it is to host a data and query logic in one place that is outside the cloud.

Once it’s done, you can send the database management systems queries to extract a subset of the information to be processed in the cloud. Keeping the DBMS core functions on-premises and moving only queries, results and exchanges in the cloud can reduce the data storage and lower its costs.

With the accepted fact that there is a considerable focus on the question of how to create hybrid clouds, it may well be that producing hybrid data will be more essential to the future of the cloud in mission-critical applications. Without a way of maximizing the use of cost-effective local storage and highly flexible cloud processing, any end-user will likely find that their large database is a dead weight that holds them within the traditional IT architectures. It will not only lose mission-critical application capital for the cloud, but it will also lose the cloud benefits for enterprises.

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