5 Big Data Trends To Watch Out For In 2018

In the last five years, big data is following digital trends and provides multiple research studies. Data is flowing faster than ever before. Big data, with the help of artificial intelligence and machine learning, generate innovative facts that lead to the growth of data sets every second.

Experts in all fields such as scientists, doctor, researchers, businesspersons, agencies face problems in dealing with such a significant amount of data. They look for robust solutions to process data generated from multiple sources such as photography, audio, IoT, retail stores, flights, robotics to develop meaningful insights. Hadoop is one such framework designed to deal with a large amounts of data by parallel computing. A comprehensive Hadoop certification training syllabus is designed by top industries experts, making it easier for you to learn big data and hadoop.

5 Big Data Trends To Watch Out For In 2018

Designing the big data architecture framework for a new system is an ongoing trend which leads to growth and functionality of the system, but it is a time-consuming and complicated process. However, when done right, the benefits of big data are immeasurable.

Given below are some of the Big Data trends that are bound to take place in 2018.

1). Big data in Medical

A number of successful case studies have come to view with the implementation of advanced machine learning terminology. An extensive database is deployed for the study while categorizing numerous user cases. These datasets are seen, manipulated and processed to find diseases and prescribe the right diagnosis.

Moreover, the data-driven approach toward heart attack has helped to precisely estimate the chances attacks through Artificial Intelligence.

2). Cloud Involvement:

Cloud and big data together have delivered multiple applications and products.The most famous product is text and voice-based bot for websites. These bots are designed by arranging the data set of possibilities. Various platforms are available out there in the market integrated with machine learning for developing products in the cloud like Polly, Azure Bot Service, face API, custom vision service, etc.

3). Sentiment Analysis:

Sentiment analysis is performed on the data gathered from the internet, social media platforms, software, etc. Data is initially in textual formats. The study involves processing, interpreting and manipulating data with the help of tools like Hadoop, etc. The analyzed information is then stored in the form of the relational database as with tuples and ordinates. The output dataset is used for prediction and future analysis with the help of data science and BI. The data set is retrieved from the platforms like facebook, twitter, google directories and other applications. The data collected from the internet as a part of the consumer or public contribution is MapReduce for the improved and better analysis.

4). Security Checkups :

Big data plays a major role in providing security to large enterprises. It helps monitor and performs analysis of the enterprise data on servers and applications. All events should be interpreted and visualized in a precise yet effective way. The data deployment technique is integrated with the encryption process for securing the data over the internet from getting compromised. Multiple APIs (Application Program Interfaces) are built to protect each node where data transfers without changing the data structure.

Security companies are working on developing the Honeypot like systems to provide the highest level of security. They collect data from the previous cyber attacks and improve the system by analyzing recorded sets with big data. So in this way, the application will automatically design an algorithm to get rid of interruption by monitoring a particular attack.

The introduction of blockchain technology in the big data removes various security hurdles with its stupendous offline storage capability. Blockchain solves the complexity associated with processing enormous data set with its relational database storage algorithms.

5). Energy and Process Industries:

Besides this, companies like Intel, Kiwi power, and its partners collect energy data for the efficient power supply to the grid. They analyze existing on-premise datasets to fulfill energy supply-demand of consumers. They forecast the power load because they have distributed system of supply with a vast number of end consumers.

The petroleum industry is dealing with trillions of datasets being generated every hour by upstream and downstream sectors from rig operations, storage, and stimulations to refining process and transportations. Tools used to ease the effort in such sectors like black oil, petrel, SCADA, etc. generates the significant amount of data every second. They analyze a considerable amount of data to maximize the throughput, production, predicting the future market, supply and distribution (S&D) chain and financial assets.

So we see how big is big data. It also plays a significant role in the retail industry to produce actionable information in an organized way like in tabular formats. The collected data is beneficial in predicting and delivering results that optimize a company’s growth and development.

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