Big Data In Blockchain
We all must have to understand both the technologies are dealing with data. Blockchain is in terms of ensuring the data storage, privacy and hence to increase the data integrity, where as the big data deals with data in terms of ensuring the data quantity, variety ,velocity in order to bring better predictions.
So simply we can say like this,
Big Data Ensures Data Quantity
Blockchain Ensures the Data Quality
Quantity and Quality are very important when the handling of data is large hence risky. So, the only place where these two technologies can meet each other is, if there is a need of ensuring both data quality and data quantity. Large scale businesses like GAFA’s ( Google, Amazon, Facebook and Apple) are often facing inconvenience in data management, as they are in the control of huge data. So this kind of business are must have to deal with two big giants on time to ensure the security level of their business data and drive their business in the right direction with better predictions.
Big Data Challenges and How Blockchain Could Resolve the Challenges ?
Inorder to understand the impact of blockchain in big data, We just have to understand the challenges of big data. Just like every other industry, big data is also facing some technical challenges and looking for a better solution that could pave the way for it. Big data is already in the top of the superior level technologies list, so in order to enhance such technology and driving it in a smooth manner, there must be a another superior level technology. So, while finding a way to resolve the challenges in big data industry there is no way, only blockchain has found to be the pain killer for the risks in data science. So, the big data industry ought to incorporate blockchain for better data analysis.
Big Data Challenges
- Security Challenge
Data’s are highly insecure when they stored in a centralized server,so there is a big chance for data hacks to occur while we handing large amount of data in central cloud servers.
- Handling The Quantity
The word big data is enough to explain this, as day passes, the floating of data attains an exponential growth everywhere. This becomes the risk for enterprises to handle data in one single or in multiple servers.
- Identifying the Malicious Data
As the data’s are in heavy in volume, it is very time consumptive to segmentize the dirty data and filter out the real data. This is like finding a black paper in a dark room.
According to a report by kaggle on 2017 among 16,000+ data professionals, 49.4% of answers denoted that the biggest challenge of data science industry is identifying the inclusion of dirty data ( unwanted data).
- Understanding The Type of Data
Big data is a collection of structured and unstructured data. In just 10 years we have gone through a massive digital growth. People are using smart devices, website and applications, hence this results in inefficiency of identifying what kind of data is, and segmenting it into a certain data panel.
- Innovation of New kind Of Data analytics Tools and Softwares
As data’s are getting varied in volume,velocity, and variety, big data scientist have to adopt with a big data tool that could be apt for handing a certain kind of data sets. Check out the top 20 big data tools 2019.
- Meeting the Time frame
Data have to be handled on time, because every second the world is generating billions of data in terabytes. So if we missed out a milli second the data will be dumped in to existing data sets. According to big data analytics it is a very risky from the beginning, data analyst have to analyze data on time to bring better predictions.
- Cost Management
Businesses have to invest a separate amount in order to setup a data warehouse and proceed data acquisition, ETL, data validation, data modeling, data visualization and etc. Whenever the data floating increases it increases the cost of data Management.
Check out how blockchain can resolve the risks and challenges in big data with its unique use cases