We need Big Data Processing Technologies to Analyze this huge amount of Real-time data and come up with Conclusions and Predictions to reduce the risks in the future.
Now let us have a look at the Categories in which the Big Data Technologies are classified:
Types of Big Data Technologies:
Big Data Technology is mainly classified into two types:
Operational Big Data Technologies
Analytical Big Data Technologies
Big-data-technologies-types
Firstly, Operational Big Data is all about the normal day-to-day data that we generate. This could be the Online Transactions, Social Media, or the data from a Particular Organization, etc. You can even consider this to be a kind of Raw Data that is used to feed the Analytical Big Data Technologies.
A few examples of Operational Big Data Technologies are as follows:
Operational-Big-Data-Technologies
Online ticket bookings, which include your Rail tickets, Flight tickets, movie tickets, etc.
Online shopping is your Amazon, Flipkart, Walmart, Snap deal, and many more.
Data from social media sites like Facebook, Instagram, what’s an app, and a lot more.
The employee details of any Multinational Company.
So, with this, let us move into the Analytical Big Data Technologies.
Analytical Big Data is like the advanced version of Big Data Technologies. It is a little harder than the Operational Big Data. In short, Analytical big data is where the actual performance part comes into the picture and the crucial real-time business decisions are made by analyzing the Operational Big Data.
A few examples of Analytical Big Data Technologies are as follows:
Stock marketing
Carrying out the Space missions where every single bit of information is crucial.
Weather forecast information.
Medical fields where a particular patient's health status can be monitored.
Let us have a look at the top Big Data Technologies being used in the IT Industries.
Top Big Data Technologies
Top big data technologies are divided into 4 fields, which are classified as follows:
Data Storage
Data Mining
Data Analytics
Data Visualization
Now let us deal with the technologies falling under each of these categories with their facts and capabilities, along with the companies which are using them.
Let us get started with Big Data Technologies in Data Storage.
Hadoop:
Hadoop Framework was designed to store and process data in a Distributed Data Processing Environment with commodity hardware with a simple programming model. It can Store and Analyze the data present in different machines with High Speeds and Low Costs.
MongoDB:
The NoSQL Document Databases, like MongoDB, offer a direct alternative to the rigid schema used in Relational Databases. This allows MongoDB to offer Flexibility while handling a wide variety of Data types, at large volumes and across Distributed Architecture.
RAINSTORM:
RAINSTORM is a software company that developed a Database Management System of the same name designed to Manage and Analyze Big Data for large enterprises. It uses reduplication Techniques to organize the process of storing large amounts of data for reference.
Hunk:
Hunk lets you access data in remote Hadoop Clusters through virtual indexes and lets you use the Splunk Search Processing Language to analyze your data. With Hunk, you can Report and Visualize large amounts from your Hadoop and NoSQL data sources.
Data Mining:
Presto:
Presto is an open-source Distributed SQL Query Engine for running Interactive Analytic Queries against data sources of all sizes, ranging from Gigabytes to Petabytes. Presto allows querying data in Hive, Cassandra, Relational Databases, and Proprietary Data Stores.
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