Trends in big data analytics
Big data today is a reality of technology world today. It is no more a buzzword but stands firmly on the shoulders of data driven decisions and modern day technologies, which have become data intensive. IDC reports states that by the year 2020, big data analytics would be a 203 billion dollar industry. A colossal rise that would see several threads of big data analytics such as intelligent apps, predicitive analytics and edge computing spread their wings. By the means of this article on the trends in big data analytics we would like to talk about the emerging topics which would make it in the year 2017.
Open source: Open source is at the forefront of big data analytics revolution. Hadoop, Spark and Apache make for the majority of big data space and will lead the future as well. Forrester claimed that Hadoop usage is increasing by 32.9 % every year. Open source remains the key to big data analytics purely due to the flexibility and wide adoption rate it has seen. Big data analytics and processing would widely adopt to Hadoop and NoSQL as the platforms of future. Another trend would be to seek out technologies that can enable big data analytics and data processing in real time.
In memory technologies: Traditionally, data has been stored in hard disk drives and ROM’s. Another recent update in this front has been the emergence of solid state drives which although fast still remain a costly way of storing and accessing data. The latest in this front to enable big data analytics greatly would be the use of In-memory technologies. These devices store and read information like random access memory, which is multiple folds faster than, read only memory.
Predictive analytics: The old method of which companies adopted to unravel the big data was looking at data and then applying analytics engine to make sense of it. Modern day machine learning approach has slashed this barrier by introducing self learning engines which can simultaneous process big data and apply analytics tools to make sense of it. This trend is going to rise with sophisticated machine learning approaches and engines in development.
Intelligent apps: Based on the learning outcomes which are derived from predictive analysis, organisations are trying to develop insights via prescriptive analytics. This is where the role of intelligent apps starts which would be useful in analysing user behaviour and providing personalisation when it comes to the services rendered by organisations. Virtually all the apps in the technology sphere would employ AI at some level to provide better services to its users. One such current example is the music and shopping apps which use AI features to provide their users with better service.
Edge computing: With the rise of IoT devices and services in general, the cloud-computing concept is going to become abstract. ‘Fog’ computing or edge computing which occurs near the IoT devices and users would become more reasonable. The concept isn’t too far-fetched as the speed of data processing and delivery is far more superior than the centralised concept of cloud. The performance is driven by the limited amount of time IoT devices need the information for and after which it is deleted. This makes the processing speedier and lays fewer burdens on the data and network infrastructure. Making the big data analytics and decision making process faster.