Wednesday, 30 November 2016

Big Data Challenges

Hi Assalamualaikum and have a good day everyone!

So this is my first time reviewing a journal and its a bit challenging. The journal that I will review about is something out of my league. I'm not going to write about makeup. So for this time being, I will make a bit different where I decided to write about non easy thingy but more to share little knowledge that might be some of my friends out there are well-known about.

I choose Big Data Challenges as this might be a challenge for me to write or give comments and my personal opinion about it. For me, this was a perfect one as I couldn't do the same as they did! Still, I can share with you guys what's from my point of view about this journal article.

The first thing I can relate when people ask me what big data's meaning is, everything in excel sheet that I manage to collect their information and details to compile when there's a contest. But its totally more than that. 
Let me tell you what big data is. It can be found in so many types such as:
1. Logs
2. Mobile
3. Banking transaction
4. Online user generated content [ ok its like what you have post in your blog spot, your daily tweets, what you have search on your google and also from satellite images! ] 
Big data is a famous phrase to describe as MASSIVE VOLUME of both structured and unstructured data. You'll be in big trouble to process big data only if you are still using traditional database software method.

So what are the Challenges? 
1. Heterogeneity (i have googled earlier and I found that the meaning is same as how you know what is Diversity) & Incompleteness

They actually face more technical issues eventhough this big data had benefits all us. When talk about big data, it talks about even every little things that must to think and makesure its work on When talk about big data, it talks about even every little things that must to think and makesure its work on it. As I've mentioned earlier, big data contains structured and unstructured. Same goes in their challenges, in order to have an efficient representation, access and analysis, they require continuously work.

It also couldn't straightly auto generate the right metadata to describe what/how data is recorded and measured. It also because lack of coordination between database system, which host the data.

2. Scale
Currently, there is fundamental shift happening. Data volume is scaling faster than compute resources, and CPU speed are static. 

3. Timeliness
Good to know, to analyze the large data needs longer time. Everytime needs to design new structures to meet the specification and to support such criteria. Plus, designing new structures is not an easy task as they becomes challenging when the data volume is growing rapidly and the queries have tight response time limits. 
When writing this review, it is really drive me to do something new and at the same time gained so much new words that I haven't discovered before. Basically from this new input, i need to think and re-read few times to understand and clear about that. Its a beautiful knowledge. So I hope I have contribute some knowledge that some of us might be never know and its good to know, you know? ☺

#bigdata #bigdataanalytics #internetofthings