Easy ways to get started with big data analytics

It is undeniable that when considering big data analytics, you are likely to get panicky and may even hire a couple of quants and data scientists, who can help you in taking the first step. However, if you have the right platform with you, availing the services of these professionals may no longer be a necessity especially if the platform brings along some pre-defined modules. Interestingly, if the platform is good enough and has an MPP (read: massively parallel processing) architecture, you may even be able to take the complexity out of analytics. Last but not least, even things like pattern matching and path analysis can be handled easily with the aid of the right platform.

Identifying the platform

When looking for a suitable platform, automatic parallelization is the first and foremost thing that must be taken into account, followed by 100% embedded processing. At the same time, it is important that all hardware and network resources are optimally utilized. However, this will only be possible if both data loading and querying are parallelized by the platform that you have selected. In fact, even backups and recoveries may play in important role in identifying the right platform; not to mention, you cannot ignore installs and upgrades as well, and must ensure that even they will undergo parallelization, if required.

Things to consider

If you have been using some data warehousing products, you may want to integrate them with the platform that you choose, and it is for this reason that you must specifically look for a suitable adaptor. Nevertheless, once you are sure that there is an adaptor in place, you can stop worrying about what will happen after deployment, but at the same time, you have to be considerate of analytic richness, which cannot be sidelined under any circumstances. Of course, if the platform is not able to help you in realizing your goal of richer analytics, then you may not be choosing the best available option.

Be careful when deciding

Finding a suitable platform may make it easy for you get started with big data analysis, but you’ll only be able to do the needful if you make the right choice when deciding. For example, if possible, you must find out what kind of returns did the others get when they used the same platform in the hope of a greater analytic insight. Likewise, if you do not see MapReduce anywhere in the picture, then maybe you are deciding too soon.