Introduction 

The expansion of big data technologies in the last three years has been exceptionally phenomenal. Be it Hadoop, Spark or Hive, the advancement of big data technologies has taken place through multiple methods and tools. It is a fact that at least 10 novel kinds of big data technologies are making their entry into the data market each month. The epicenter of all big data technologies is data analytics. By virtue of data analytics, data mining, data processing and data visualization have become possible. This has not only motivated the business professionals to take up data analytics certifications but has also convinced the companies about the inevitability of this technology in the future.

In this article, we review some of the novel big data technologies that have created an impact in the age of analytics.

Categorizing big data technologies 

There are primarily two main classifications related to big data technologies. The first classification is called operational big data technology. As the name indicates, this type of technology focuses on the analysis of large amounts of data that is generated every moment. The main aim of this technology is to conceive such software which can be used for the analysis of raw data in order to derive substantial analytics from it. Such types of operational data technologies are used by the largest tech giants of the world including Amazon and Walmart. This technology allows these companies to manage data effectively and efficiently and keep track of the same.

Another important classification related to big data technology is called analytical technology. Analytical big data technology is simply an advancement over operational big data technology. The main aim of this technology is to carry outer real time analytics and provide essential insights for the purpose of decision making. This kind of real time analytics is relevant for application domains like stock marketing and other fields where time series analysis of data is essential. This technology also finds application in the domain of weather forecast in. Error free analysis of medical records is also possible with the help of this technology.

A review of novel big data technologies 

There is no doubt in the fact that more than 100 big data technologies have been tested in the last decade. However, not all of them have made it from the production stage to the market stage. Only a handful of big data technologies have become successful enough to be incorporated by large businesses for the purpose of analytics.

TensorFlow

The first important technology among the list is TensorFlow. The main advantage of using this technology is that it allows the researchers to work with a diversity of tools and resources so that effective analytics is derived in a short span of time. This type of technology helps in the deployment of machine learning applications in a hassle free manner. 

Beam and docker

In addition to this, beam and docker are other two important big data technologies that have emerged post 2016. While beam is an extremely effective tool when it comes to application programming interface, docker allows the development as well as deployment of container application in a simplified manner. Beam makes it very easy to manage data processing pipelines. Similarly, docker makes it possible to stack an application with various components.

Kubernetes 

One of the most important open source tools that has become popular in the domain of big data technologies is kubernetes. This technology provides a customized platform for deployment and execution of containers with the help of clusters. Kubernetes was originally a brainchild of Google and was developed for the sole purpose of container management. Later on, it came to have many other applications as well.

Blockchain and big data technologies 

Blockchain has been one of the most fascinating big data technologies that has created a buzz in the business world. Although a large number of big data technologies are available, the need for such a technology was felt that could make our data transactions secure. Data privacy, data security, data sensitivity and data breaches started to gather the attention of various businesses as well as researchers. This was extremely important from the point of view of the fintech industry.

Structurally, blockchain consists of a series of ledgers that are arranged parallely and the transaction records in one block or ledger simultaneously induce changes in all the ledgers. This makes it virtually impossible to tamper with the original records. Hence, the deployment of blockchain technology makes financial transactions absolutely foolproof, safe and secure. The security dependance on blockchain technology has given rise to the popularity of cryptocurrencies around the world. 

El Salvador has become the first country to legalize cryptocurrency. This trend may soon spread to other countries in Asia and Africa. While in India, there is a call to use the blockchain technology for keeping a ledger of land records. In addition to this, other applications of blockchain technology are also surfacing slowly. In the future, we may expect blockchain technology to become even more popular given the strong security architecture that it brings with itself.

Future prospects and the way ahead 

In the future, we would see continuous integration of big data technology with other emerging technologies so that this combination becomes an epicenter for a large number of application domains. For instance, the integration of cloud based technology with big data technology is already on the cards. Big Data Analytics is being carried out in the cloud interface with the help of various services offered by cloud computing. Such services include infrastructure as a service, platform as a service and software as a service. In the future, we are likely to witness the emergence of data as a service. Data as a service would provide the perfect interface where the application domains of big data technology and cloud solutions coincide.

Conclusion 

We are likely to witness a period in which companies drift towards self service solutions. This would signify a further advancement of big data technologies and the start of an autonomous age in which professionals are equipped with simple yet advanced tools to usher and work in multiple domains and environments. 

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