The Best Of Big Data Analytics Is Yet To Come: Analyzing Trends And Prospects
Big data technologies are the most popular research domains in the present times. In simple terms, big data refers to the voluminous amount of data that is generated from thousands of smart devices but is difficult to process using the traditional technologies. It is in this context that the research in Big Data Technologies related to mining of valuable data sets and derivation of critical insights assumes pivotal importance.
As the domain of big data analytics is on the rise, the technologies as well as courses related to it are also expanding constantly. The research prospects of the latest technologies as well as online big data courses are significantly contributing to the analytical dimensions.
Let us classify big data technologies and analyze the various trends in this domain.
Nomenclature and classification
We can broadly divide big data technologies into two types. The first is called operational type and the second is called analytical type.
Operational type
The operational aspects of big data technologies indicate the voluminous amount of data that is generated from various sources and processes. The different types of sources include thousands of smart devices that operate in the environs of a smart city. This also includes the internet of things. This source is particularly important as it creates a fabric of data that is very difficult to analyze using the traditional means and methods. The different types of processes include online transactions, purchases, customer history, customer recommendations, social media postings or any other type of data generation that is carried out by a software company.
Analytical type
The analytics type of big data technology includes advanced types of processing and insights. This process usually transforms data from its raw format to a relatively structured format. This type of analytics is used in weather forecasting, analysis of medical records, stock market analysis and the like. It is also used for keeping a record of financial transactions and deriving critical insights from this data.
Trends and prospects
Let’s take a look at some of the most important trends and prospects of big data technologies that we would see in the coming times.
Hadoop remains the simplest available option
The primary aim of developing the Hadoop ecosystem was to provide a simple programming model that could function in a distributed environment. Before the advent of the Hadoop ecosystem, companies used to prefer various types of data warehouses. The data was gradually channelized from these warehouses if it was required for the process of analytics. However, this was a time consuming option with low latency and high downtime. The Hadoop ecosystem did away with the need of the traditional warehouses and provided advanced analytics capabilities. In the future, we may expect that it remains the simplest option available to new startups who are looking to make a mark in the digital industry.
Artificial intelligence can prove to be a game changer
The entire process of Big Data Analytics takes place in a number of stages. The first is related to mining of data and converting it from unstructured format to a structured format. The second stage is related to reducing the dimensionality of large data sets on the basis of certain attributes and parameters. The third stage takes us to the stage of analytics. After this, we move on to the stage of data visualization. It needs to be noted at this point in time that the entire process of Big Data Analytics is not only challenging but also time taking one. However, with the inputs of artificial intelligence as well as machine learning, we can automate the entire process of analytics.
Intrusion detection system: A case study
Let us consider that we are given a huge data set that contains data derived from iot sensors. We need to derive necessary insights from this data and feed this into an intrusion detection system. In order to resolve this problem statement, the traditional process of analytics can take a relatively longer time. With the help of machine learning techniques, we can sketch out the parameters that we need to work with in the intrusion detection system. Consequently, we can use techniques like principal component analysis, K means clustering, K nearest neighbors, bagging, boosting as well as other ensemble modeling techniques for choosing the right parameters related to the intrusion detection system. In this way, artificial intelligence and machine learning can go a long way in improving the various stages of big data analytics.
Concluding remarks
Tensorflow, Beam, and Docker are other emerging technologies that can rapidly contribute to big data analytics and enlarge the domain of its operation.
