Trends Driving the future of BigData in 2018
Big Data Analytics is a method of uncovering hidden patterns. The right Big Data service will help interpret unknown correlations, market trends and other necessary information. It deals with the examination of large sets of data differing in types. This process is majorly used by companies in order to maximize their business benefits.
What is the future scope of big data analytics?
In the year 2018, there have been several innovations in the field of big data analytics due to changing market requirements. Nowadays there is a requirement of platforms which supports data custodians to govern and provide security to big data even while enabling the end users to make use of data for evaluations. The trends keep on changing keeping in mind the IT standards on priority.
10 TRENDS DRIVING THE FUTURE OF BIG DATA
Keeping up with big data trends is a highly cumbersome task, the moment you become pro at using a trend, the trend will change immediately. Nevertheless, it is these changing trends that shape the future of big data. Out of many big data trends, the top 10 trends are:
- BIG DATA AND OPEN SOURCE APPLICATIONS
The use of open source applications dominate the world of big data. According to experts, many companies will enhance the use of open source applications like Hadoop and NoSQL technologies in order to enhance the speed of the processing of big data. Most of the enterprises are looking for technologies, which allow them to respond and have quick access to big data in a real-time setting.
In a survey, it was found around 60 per cent of the total enterprises have Hadoop clusters running on their system by the end of the year. The usage of Hadoop is increasing by 32.9 per cent per year.
- INVESTMENT IN MACHINE LEARNING –
Due to the latest progress among the capabilities of big data analytics, many enterprises have started making an investment in Machine Learning (ML). It is that branch of artificial intelligence, which focuses on enabling computers to learn novel things without being programmed explicitly. Its primary function is to analyze big data, which is already available in store to derive conclusions that ultimately changes the behaviour of applications.
The most advanced machine learning and artificial intelligence systems in today’s world are taking a step beyond the traditional rule-based algorithm system. This is done in order to develop a system which can easily understand, learn, predict, adapt and operate autonomously potentially from anywhere in the world.
- USE OF PREDICTIVE ANALYTICS
Predictive analytics is similar to machine learning as it makes use of big data analysis to predict future happenings. This is a very powerful tool for big data analytics. At present, there is only 29 per cent of users of Predictive analytics tools. However, in recent years Predictive analytics tools will be used by almost all enterprises due to its technological advancement.
- USE OF CLOUD HAS ENHANCED GRADUALLY-
Since there was a huge requirement to provide safe and secure access to data and tools for analytics to employees working in isolation from around the globe, Companies switched to cloud.
- THE INTERNET OF THINGS (IoT)-
“The Internet of Things” tends to have quite an impact on the big data. According to the survey report of Verizon’s State of the Market: Internet of Things 2017, “About 73% of executives are either launching or researching about IoT solutions. The report also mentioned that the IoT platform market is expected to reach US $1.6 billion by 2020 with 35% growth every year.
- USE OF IN-MEMORY TECHNOLOGY
The in-memory technology is used by companies in order to speed up the processing of big data. In the old traditional system, the data is stored in storage systems which works on hard drives or solid states drives (SSDs).
However, in the new In- memory technology stores data into RAM instead of hard drives, which many times faster in comparison to the traditional ways. In-memory technology massively shortens the query response times, allowing business intelligence (BI) and analytic applications to access large volumes of data in real-time, which helps in making business decisions.
According to the report generated by Forrester Research, the usage of In-memory data fabrics will increase every year by 29.2 per cent. There are many in-memory database technology vendors in the market such as SAP HANA (high-performance analytic appliance), International Business Machines (IBM), and Pivotal.
- INCREASED USE OF BIG DATA ANALYTICS-
The increased use of big data is driven by IP networks that connect trillions of physical devices together. This connection of devices, processes, data and people all around the globe builds the future of big data. IP has become a common language for almost every data communication especially in the field of proprietary industrial networks such as electricity grids, building systems, industrial manufacturing, oil systems and many other sectors.
8. INTRODUCTION AND USAGE OF INTERNET PROTOCOL (IP) VERSION 6-
Due to the introduction of Internet Protocol (IP) version 6 (IPv6), there has been an end to the technical limitations which was earlier levied on a number of devices that can easily connect to the internet and make data access to millions and trillions of users.
- BIG DATA INTELLIGENT APPLICATIONS–
The use of machine learning and AI technologies helps in developing intelligent applications. These applications make use of big data analytics in order to analyze the user’s past behaviour. This is further used to provide a better quality of service as well as personalization. It is believed in the next decade almost every virtual application and service will incorporate a certain level of artificial intelligence. This will further help in forming a long-term trend that will keep on evolving and expanding the application of Artificial Intelligence and machine learning.
- INTELLIGENT SECURITY
Nowadays many enterprises incorporate big data analytics into their security strategies. The security log data of companies offer a lot of information about previous cyber-attack attempts that will help enterprises to use, predict and prevent attacks in the future.