Distinctive Attributes of Big Data Platform and Big Data Analytics Software for IoT

Abstract

Data in today’s world is as valuable as money. Data science is the interfusion of concepts like data interface, algorithms development, and technology, which are used to solve analytically complex real-world problems. Data analytics is finding relevant insights from a given dataset. Data analytics includes many processes like asking relevant questions, a collection of data, forming a model, and analyzing the results. Machine learning goes hand in hand with data science. Machine learning gives the ability to a computer to learn from the information provided without any human interface. Machine learning is encompassed in the term “data science”. There are many algorithms of machine learning that are used in the model formation and also in the analysis phase. One of the major features of machine learning is feature engineering. Feature engineering uses the domain knowledge to add or create features that make algorithms work and give better results. However, now feature engineering can be applied to Internet of Things (IoT) analytics. Three separate functions are combined into a single programming tool known as ETL (extract, transform, load), which is considered as a general procedure for managing databases. Hadoop, an open-source software platform, is used for distributed storage as well as distributed processing of large datasets. It is designed for single-node clusters as well as multiple-node clusters that run on commodity hardware. With the help of IoT analytics, we can collect very critical data (streaming data as well) from sensors that can be processed using big data tools. With this data, we can form a very efficient model to get insights which will help the decision-making process for any organization. A combination of machine learning, IoT analytics, and R language can solve big data problems as well.