5 Big Data Technologies You Must Know

The use of Big Data Analytics has gone mainstream and an increasing number of companies are using it to streamline their business operations. Big Data as we know is an invaluable endowment of Data Science and it’s turning out to be a requisite requirement for every business these days.

Big Data Analytics provides suitable means for analysing and processing huge chunks of data using advanced methods like predictive modelling, data mining and Machine Learning. This data can further be used for extracting useful information like the user preferences and the price analysis. But for all we know, Big Data is not the only thing that matters seeing the current scenario. There are many other technologies associated with Big Data that can help you expand your business and make it well organized.

Given below are the five Big Data technologies you must be familiar with as a Data Engineer.

#1 Predictive Analytics

If you have your roots embedded in Data Science, then you must have heard about Predictive Analytics. The technology is used by a large number of firms all over the world for taking important decisions and making predictions of the future events. It does so by the use of various statistical techniques like machine learning, predictive modelling and data mining. Although it has a slightly different set of applications, there’s a close relation between Big Data and Predictive Analytics. It also utilizes big data resources and helps in improving business strategies.

#2 NoSQL DB

NoSQL is a database program that also finds its use in Big Data applications. The DB provides valuable means for storing Big Data (complex data sets) with an increased level of flexibility and scalability. It also offers an easy method of retrieval at the hour of need. The NoSQL databases have shown an exceptional growth over the past few years and they have completely replaced their RDBMS counterparts.

#3 Stream Analytics

Stream Analytics is an event processing engine that allows you to filter, analyse and aggregate high volumes of data. It provides event processing with high throughput and low latency. Millions of companies and organizations use Stream Analytics for gaining immediate insights into the real-time data and detecting potential points of failure.

#4 Data Virtualization Using Hadoop

Data Virtualization is an advanced method of Data Management that adds self-retrieving capabilities to the applications for effectively managing data resources of an organization. You can not only retrieve but also manipulate data if need be.

#5 Data Integration

By Data Integration, we add advanced data management techniques to the regular business operations. The method is also useful in converting data retrieved from multiple sources into useful information.