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Massive amounts of data being stored on big computers are nothing unheard of. But what has changed is the expectation and need to mine that massive amount of data for an organization to make decisions.
Hot Topics Concerning Big Data
All of the experts agree that what will mean the life or death of a business in the upcoming years is their ability to gain access to and analyze big data. So who will big data analytics be made possible? Here are the three primary ways:
- Getting rid of those old disks and DRAM and replacing them with nonvolatile memory.
- Getting rid of that old copper cabling, which will reduce space and power requirements. This means using photonics and improve interconnects.
- Reduce power and footprints on the ecosystem by introducing advances in systems on a chip.
Where to Start First
The big question every company has is, "How to take that first leap to light speed and become a master data analyzer? Should I hire outside help for this task?" But the first question actually should be, "How business-critical is the data I want to mine?"
The problem with doing some big data projects in-house is that it isn't easy. Many an IT shop attempted projects in-house, and even with all of their grit and planning, their projects failed because it simply took too much time, and specialized skill sets that were far less scarce than they are now. Today, even in 2017, big data platforms like Hortonworks and MapR can name their own price.
Answer: Keep data that is vital to your organization's survival in-house. For other analytics, you can go ahead and outsource that. Just remember that those who you outsource should just act as an offsite data warehouse, but they should also be analyzing that data for you.
When choosing to outsource your big data job, take this into consideration:
"It is also impossible to deny the major advantages of using an outsourced workforce which is the price. For example, the price tag for development services in the US can be up to $150/hr, approximately. In contrast, Eastern European pricing is set closer to $30-50/hr. Impressive difference, isn’t it? Being able to cut expenses without sacrificing the quality of work is one factor which makes outsourcing more and more financially attractive, especially for startup companies.”
That’s why it is important to find app developers who are affordable yet good at their job.
Maybe we moved too fast. See, there are five different types of data, but for this article, we will only focus on two: 1) Information that can be determined over time and 2) Data that must be mined and analyzed in real time.
Number one is what they consider a "backend" operation. You can dive deep into long-term business processing and analysis. Companies such as Hadoop and MapReduce facilitate this type of analysis. They are able to scale down information and spread it across many commodity processors. They are also equipped with built-in triple redundancy for added security. This is better suited for running in the wonderful cloud.
For the latter class of data, people usually go to technologies that support real-time and streaming processing, such as Flink, Spark, and open-source Storm. It used to take days to conduct an analysis of this sort of data, but now faster processing has been cut down to hours, and, soon, if things continue going well, can be expected to be done in minutes.
What Does the Finished Product Look Like
A great example of this would be a startup whose business is it to gather news from all over the world and publish it in their online magazine. All of that data would be scrapped and collected from news sources, social media and blogs and turned into on finished product. The data would most certainly have to be analyzed in real time - or at least near real-time as it can get - with the system alerting for specific topics and keywords to create the work. This is the type of job that should be done in-house.
Another great example would be modern marketing platforms. Large quantities of user data are gathered, from geographic location, to what they browse, share, like and so on. The end result of this is rather precise insights that can be revealed after analyzing this data, and this is the reason why modern ad platforms like Adbeat continue to expand and bring so much revenue.
Big data analytics has changed the game for the entire world - it doesn’t matter if it is governments, institutions, organizations, universities, or hospitals - and that means problems can almost be solved before they even happen.