Top trends in Big Data Industry

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Recent few years have marked a revolutionary change in big data industry from few companies storing and structuring data to more and more industries pouring data and structuring it.

According to various technology experts, 2012 – 2016 brought us on a verge of new era of big data industry where traditional big data methodologies is changing to smart real time problem solving techniques. The last few years also identified the need of change in big data industry like need of analytics, data governance and security and many more.

2017-18, will notice rise of smart, agile technologies which will actually solve the demands rising from big data users and enterprises.

  1. Changing myth of 3Vs

3Vs concept derived by Gartner, Volume, Velocity and Variety where volume and velocity has grown traditionally but variety has gone through unparalleled growth. According to the survey done by new vantage partners this trend will continue as industries will attract more integrations across the formats and sources. From relational (JSON) and Non SQL formats to flat Data sources, multiplying data formats giving rise in need and use of analytical platforms in 2017 and coming years.

  1. Rise in Demand of Analytics

According to the survey of ACM 2016, daily rise of big data sets and structures with structured and unstructured information giving rise of analytics for faster access of values and information for predictive analysis, BI for better understanding of businesses or any other sector.

Changing Fintech industry needs regular predictive analysis for its growth and stability, rising transformation needs behavioral analysis and predictive analysis of customers and market trends for its growth and involvement transportation evolution.

To these demand s of analytics various computing AI based platforms and self service platforms will trend in coming year 2017 and later. Microsoft Azure ML which integrates with existing Microsoft platforms has been insight driven for change in model preparation for more self driven platforms. More companies like Paxata, Alteryx and Trifacta which preps both Hadoop and non hadoop based data formats is bringing thinking to reality by simplification of complexity by preparing data for analysis.

Top 10 Tools in Big data industry

Data is piling up day by day so as the complexity, Big data industry has established itself a biggest need for all businesses. Every business entity needs a deep analysis of its data for better understanding of its target audience and its approach towards its product or services. In today’s era of target driven approach has made analytics the essential need of any business where they need a regular simplification of complex behavior of data for achieving their goals.

Big data industry not only impacts the goals but also it helps in making the strategies to achieve the decided goals for businesses working on various sectors.

Big data has various formats structured, semi structure and unstructured raw data sets, to extract the value for particular purpose we need various technologies and tools to achieve Business intelligence.

This Article is about the top 10 big data tools used by various businesses.

  1. Apache Cassandra

Apache Cassandra also termed as Cassandra is one of the most useful tools for NoSql databases. It is an open source platform designed for nosql large databases without any failure. It is fast, scalable, serves low level latency for all its clients.

Cassandra is decentralized, scalable, multi datacenter replication, consistent and fault tolerant.

Cassandra supports MapReduce, Apache pig, Apache hive as it has hadoop integration.

  1. Hadoop

Hadoop is an open source framework supported by apache software with Mapreduce programming model. It stores heavy data sets and runs application on clusters of commodity hardware and supports various tasks and jobs.

Hadoop allows distributed databases processing on clusters makes it different from others and that’s why it’s being used by large number of corporate for their data processing.

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