Introduction
It is popularly said that Big data is not just technology in the present times but a rapid transformative process. This transformative process involves the processing and analysis of extremely complex and unstructured data sets. That said, this is a task that is easier said than done. With a daily uptake of almost 2.5 quintillion bytes, the rate of growth of big data is difficult to scale. Professionals with years of big data training experience opine that the current rate of growth of the big data market would scale to 450 billion dollars by 2026. Thus, there is a dire need to conceive and develop technologies that can cater to the ever-growing juggernaut of data. In this article, we deep dive into some of these technologies.
Operational and analytical technologies
Operational big data technology involves processing thousands of online transactions and tracking information on social media. It is used by large E-Commerce firms as well as business intelligence companies. Operational aspects of big data technologies are particularly important for predictive analytics, product recommendation, customer targeting, and price estimation. The analytical big data technologies are usually suitable for such business categories that involve advanced analytics.
For instance, the decision-making process of a company is increasingly dependent upon analytical big data technology. Similarly, analytical big data technologies find application in stock marketing and time series analysis of data. It can also be used for weather forecasting and prediction of extreme climate events. The formation of the National Health Stack is a direct outcome of analytical big data technologies. It is also used for the analysis of medical records as well as their continuous updating.
Chatbot technology
Chatbot Technology makes use of big data analytics by processing human language in the form of discrete data sets. To make it much simpler, we can think of chatbot technology as an application of artificial intelligence on linguistic data. The most central aspect of chatbots presently is the analysis of human-computer interaction. This interaction involves real-time analytics of large data sets as well as the application of reinforcement learning. The purpose of reinforcement learning that is used in chatbot technology allows the chatbot to learn from its environment at the first instance. After this period, equal iterations are performed to correct the chatbot using some limited but labeled data sets.
Database Management
A large number of big data technologies are utilized for the analysis of large data sets that are stored in various databases. For the purpose of data acquisition, a set of operations are performed on a non-relational database. Real-time operations are performed to convert unstructured data sets into structured data sets. The main purpose of this conversion is to organize and integrate different data sets so that they can be horizontally scaled. This not only ensures effective database management but also boosts performance and flexibility. Needless to mention, different types of data structures are mined from various databases so that computations can be performed with a lot of ease.
Data lakes
Data lakes can be referred to as a repository that stores both structured as well as unstructured data sets at a single station. The most important advantage of a data lake is that it allows us to store data without transforming it. At a subsequent stage, we can carry out an analysis of unstructured data sets. Real-time processing can also be carried out in addition to other data operations as and when required.
Data lakes are extremely helpful for businesses as they can store volumes of information in them for usage at a future date. Advanced analytics with the help of machine learning techniques can be carried out on data lakes. Data that is extracted can be portrayed in the form of attractive dashboards and visualization methods.
The way ahead: New generation technologies
Tensorflow, beam, docker, and airflow provide an ecosystem of tools and techniques that help analyze unstructured data sets in a short span of time. Different types of machine learning techniques are deployed for this purpose. Sophisticated, advanced, and organized data processing can be achieved with the help of beam. Similarly, the most important big data technology that simplifies container applications is docker.
In addition to this, airflow provides us with an advanced management system that not only helps in future scheduling but also in managing various data pipelines. The idea of blockchain is in perfect juxtaposition with big data technologies. Because it provides one of the unique ways of not only storing data but also encrypting it at the same time. It is due to this reason that blockchain technology has been employed in the banking, finance, and insurance industry that is extremely cautious about cyber security.
