Emerging Technologies for Big Data

Emerging Technologies for Big Data

Emerging-Technologies-for-Big-Data-NareshitEvery day huge amount of data is generated all over the world (Data might be in petabytes). Data large in size is called Big Data. Normally people use to work on data of size MB (small documents) and GB (coding, animation coding) but data in peta bytes is called Big Data. Big data might come various organizations like Facebook, Yahoo, weather stations, Telecom companies, Google etc.
There are many challenges of dealing with big data, they are;
  • Huge amount of investment is required to procure high processing servers.
  • Amount of time taken process huge amount of data
  • Rectifying error in huge code would be impossible
  • Difficult to build query
There are many organizations that are working to develop new technologies that concentrate on big data. Let’s read about emerging technologies for big data.

Emerging Technologies for Big Data

Column-oriented databases

Here are dealing with huge amount of data may be in peta bytes. The conventional database works well with limited data due to its fast online transaction processing and high-speed updates. With every grow data performance of traditional database reduces drastically. Unstructured data makes it difficult for the traditional database to maintain same speed. Data compression makes processing time faster as data is stores with focus on columns.

NoSQL database

Databases like key-value stores and document stores focuses on storing and retrieval of large volumes of unstructured, semi-structured and structured data. Unlike traditional databases, Read consistency is not perfect in these types of databases.

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MapReduce is a programming paradigm that allows massive job execution scalability. Job executions are performed against cluster of servers. MapReduce has two tasks that are;
  • “Map” task convert input dataset into different set of keys or tuples
  • “ Reduce” task combine various “Map” outputs into reduced tuples


Hadoop is an open source java based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. Sponsored by Apache software foundation, Hadoop is a part of Apache project. Hadoop is used for offline processing. Hadoop is being used by Facebook, Yahoo, Google, Twitter, LinkedIn and many more organizations. World is getting flooded with cutting-edge big data technologies and Hadoop is one of them.


Hive is open source data warehouse software project that provides data summarization, query and analysis. Hive is used on Hadoop for analysis of large datasets. It supports large datasets that are stored on Hadoop servers. Hive supports various storage types that are plain text, RCFile, HBase,ORC and many more.


Hadoop’s MapReduce work is minimal while working with Hadoop’s storage. Developers must have extensive knowledge to operate. Generally it takes hours to create, test and run various jobs. Platfora is a platform that turns users’ queries into Hadoop jobs automatically. This feature of Platfora help users to simplify and organize datasets stored in Hadoop.


SkyTree is machine learning and data analytics platform that offers various services on handling big data. Hadoop deals with huge amount of data so manual exploration is required as automatic exploration will be expensive and time-consuming.
Dealing with big data is quite a daunting task. Emerging technologies for big data helps in understanding methods to deal with it. Big data is all about dealing with petabytes of data and Hadoop is the right option for you.

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