Big Data is a field that treats ways to analyze, systematically extract information from or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
Big Data technology is the most popular word which you get to hear much in the recent days.
Here, we will discuss about the technologies which made Big Data to reach greater heights.
Big Data Technology can be defined as a Software Utility that is designed to analyse, process and extract the information from an extremely complex and large data sets which the Traditional Data Processing Software could never deal with.
We need Big Data Processing Technologies to analyse this huge amount of Real-time data and come up with conclusions and predictions to reduce the risks in the future.
Big Data Technology is mainly classified into two types:
1. Operational Big Data Technologies
2. Analytical Big Data Technologies
Operational Big Data Technologies:
The Operational Big Data is all about the normal day to day data that is generated. This could be the online transactions, social media or the data from a particular organisation etc. A few examples of Operational Big Data Technologies are as follows:
1. Online ticket bookings which includes your Rail tickets, Flight tickets, movie tickets etc.
2. Online shopping which is your Amazon, Flipkart, Snap deal and many more.
3. Data from social media sites like Facebook, Instagram, what’s app and a lot more.
Analytical Big Data Technologies:
Analytical Big Data is like the advanced version of Big Data Technologies.
It is a little complex 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.
Few examples of Analytical Big Data Technologies are as follows:
1. Stock marketing
2.Carrying out the Space missions where every single bit of information is crucial.
3. Weather forecast information.
4. Medical fields where a particular patient’s health status can be monitored.
Top Eight Big Data Tools
1. Apache Hadoop
Apache Hadoop is a java based free software framework that can effectively store large amount of data in a cluster.
This framework runs in parallel on a cluster and has an ability to allow us to process data across all nodes.
Hadoop Distributed File System (HDFS) is the storage system of Hadoop which splits big data and distribute across many nodes in a cluster.
This also replicates data in a cluster thus providing high availability.
2. Microsoft HDInsight
It is a Big Data solution from Microsoft powered by Apache Hadoop which is available as a service in the cloud.
HDInsight uses Windows Azure Blob storage as the default file system. This also provides high availability with low cost.
While the traditional SQL can be effectively used to handle large amount of structured data, we need NoSQL (Not Only SQL) to handle unstructured data.
NoSQL databases store unstructured data with no particular schema.
NoSQL gives better performance in storing massive amount of data.
This is a distributed data management for Hadoop. This supports SQL-like query option HiveSQL (HSQL) to access big data.
This can be primarily used for Data mining purpose. This runs on top of Hadoop.
This is a tool that connects Hadoop with various relational databases to transfer data.
This can be effectively used to transfer structured data to Hadoop or Hive.
This works on top of SQL Server and is used to access data stored in PDW.
PDW is a data warhousing appliance built for processing any volume of relational data and provides an integration with Hadoop allowing us to access non-relational data as well.
7. Big data in EXCEL
As many people are comfortable in doing analysis in EXCEL, a popular tool from Microsoft, you can also connect data stored in Hadoop using EXCEL.
Horton Works, which is primarily working in providing Enterprise Apache Hadoop, provides an option to access big data stored in their Hadoop platform using EXCEL.
Facebook has developed and recently open-sourced its Query engine (SQL-on-Hadoop) named Presto which is built to handle peta bytes of data.
Unlike Hive, Presto does not depend on Map Reduce technique and can quickly retrieve data.
Future scope and development:
Today, Big Data is influencing IT industry like few technologies have done before. The massive data generated from mobile devices, cloud computing, social media, satellites help different organizations improve their decision making and take their business to another level.
Google is launching the Google Cloud Platform, which provides developers to develop a range of products from simple websites to complex applications.
It enables users to launch virtual machines, store huge amount of data online and many other things. Basically, it will be an one stop platform for online gaming, mobile applications, etc.
All these require huge amount of data processing where Big Data plays an immense role in data processing. So, one must also have a bright future in Big Data.