The Changing Landscape Of Big Data Processing

Just one year ago, the tech community was lamenting about how it would deal with all of the data that needed to be processed. The Big Data conundrum is that there was more data being processed on a daily basis than could be processed in a timely matter. Tools and techniques have been the focus where the question of how to deal with Big Data is concerned.

Quality Management

Big Data’s ability to be handled efficiently has much to do with the management of data quality. As the volume of data grows along with data sources, the speed at which these are processed must also grow, but not at the cost of management quality. To help with this, experts say that we may be able to get even more out of the techniques and tools we are using today. One example is data profiling tools, which can be used to audit data sources which are both internal and external. These profiling tools can also be used to obtain those useful bits of information which are located in a sea of unnecessary info.

However, it isn’t only the use of existing data processing tools that need to evolve, say experts; it’s also the tools themselves which need to evolve. Data processing tools need to be able to process more data at faster speeds than they used to. This doesn’t negate older data tools, however; these can still be used to process smaller volumes of data needing more thorough examination.

Big Data Management in the IT Sector

Today’s data center is much different than before, with cloud computing being used extensively. This has become a way for IT management to expand their analytics offerings to a wide range of businesses. But the ability to connect to an application in the cloud isn’t enough, experts say. Instead, any solution that can be deemed effective in IT operations must be able to not only exist in the cloud, but be integrated with other cloud data, meaning that it would have to be able to operate completely independently in a cloud environment.

But this type of system must also be able to work with applications and tools that are not in the cloud. This has seen many IT systems which are able to work across multiple operating systems, infrastructure types, databases and languages. But if systems like this are not kept running at peak efficiency 100% of the time, it becomes necessary to have some sort of solution which can interface with this high number of tools, and not at the expense of the resources or time of the IT department itself.

Real-Time Processing

In the spirit of delivering the rich applications that many businesses need, one solution to handling the immense volume of Big Data being created on a daily basis is to marry it with real-time processing. This has resulted in several interesting solutions being introduced to the market. Some companies have elected to create their own cloud in which all data processing occurs. Others have opted to rent the services of analytics companies, which themselves use several different sources for processing data in real time. Other data processing companies have combined their available services to offer companies industrial-strength processing at required speeds. Still others have turned to open-source code and created their own versions of efficient data processing solutions.

The question remains whether today’s solutions for the processing of Big Data will be enough to process the volume of data that companies are creating. Some say that technology from even two years ago will not be able to keep up with the growing amount of data needing processing. But there is hope; some of today’s data processing systems are able to process data in minutes that would take some systems months to navigate.

About the Author

Guest author Leah Messina writes on a variety of technology-related topics. She recommends HighSpeed Internet Providers as a resource for consumers looking for information on broadband.

Citations: How Big Data is Changing IT
Photo credit: Andrea Balzano