Big Data Analytics: A Brief Survey

In recent days, the size of the informations generated from modern information systems and digital technologies like IoT and Cloud computing is huge (ie. In TB). With this huge sized data, it is quite difficult to analysis and it is in the need of more effects at multiple levels to extract data. Big analysis is the technique and used both for research and development. The idea of this paper is to give the brief about the big data concepts. Additionally, it will support for the researchers who is doing their research in the area of big data.


INTRODUCTION
In digital world, data are generated from various sources and the fast transition from digital technologies has led to growth of big data. It provides evolutionary breakthroughs in many fiel collection of large datasets. In general, it refers to the collection of large and complex datasets which are difficult to process using traditional database management tools or data processing applications. These are available in structured, semi-structured, and unstructured format in pet bytes and beyond. Formally, it is defined from 3Vs to 4Vs. 3Vs refers to volume, velocity, and variety. Volume refers to the huge amount of data that are being generated everyday whereas velocity is the rate of growth and how fast the data are gathered for being analysis. Variety provides information about the types of data such as structured, unstructured, semi structured etc. The fourth V refers to veracity that includes availability and accountability. The prime objective of big data analysis is to process data of high volume, velocity, variety, and veracity using various In recent days, the size of the informations generated modern information systems and digital technologies like IoT and Cloud computing is huge (ie. In TB). With this huge sized data, it is quite difficult to analysis and it is in the need of more effects at multiple levels to extract data. Big data s the technique and used both for research and development. The idea of this paper is to give the brief about the big data concepts. Additionally, it will support for the researchers who is doing their research analytics; Massive data; In digital world, data are generated from various sources and the fast transition from digital technologies has led to growth of big data. It provides ny fields with collection of large datasets. In general, it refers to the collection of large and complex datasets which are difficult to process using traditional database management tools or data processing applications. structured, and unstructured format in pet bytes and beyond. Formally, it is defined from 3Vs to 4Vs. 3Vs refers to volume, velocity, and variety. Volume refers to the huge amount of data that are being generated e of growth and how fast the data are gathered for being analysis. Variety provides information about the types of data such as structured, unstructured, semi structured etc. The fourth V refers to veracity that includes e prime objective of big data analysis is to process data of high volume, velocity, variety, and veracity using various traditional and computational intelligent techniques [1]. Some of these extraction methods for obtaining helpful information was discu Haider [2]. The following Figure 1 refers to the definition of big data. However exact definition for big data is not defined and there is a believe problem specific. This will help us in obtaining enhanced decision making, i optimization while being innovative and cost effective. It is expected that the growth of big data is estimated to reach 25 billion by 2015 [3]. From the perspective of the information and communication technology, big data is a robust generation of information technology industries [4] which are broadly built on the third platform, mainly referring to big data, cloud computing, internet of things, and social business. Generally, Data warehouses have been used to ma dataset. In this case extracting the precise knowledge from the available big data is a foremost issue. Most of the presented approaches in data mining are not usually able to handle the large datasets successfully.
The key problem in the analysis of big data is the lack of coordination between database systems as well as with analysis tools such as data mining and statistical analysis. These challenges generally arise when we wish to perform knowledge discovery and representation for its practical applications. A fundamental problem is how to quantitatively describe the essential characteristics of big data. There is a need for epistemological implications in describing data revolution [5]. Additionally, the study on complexity theory of big data will help understand essential characteristics and formation of complex patterns in big data, simplify its representation, gets better knowledge abstraction, and guide the design of Jun  [1]. Some of these extraction methods for obtaining helpful information was discussed by Gandomi and [2]. The following Figure 1 refers to the definition of big data. However exact definition for there is a believe that it is problem specific. This will help us in obtaining enhanced decision making, insight discovery and optimization while being innovative and costeffective. It is expected that the growth of big data is estimated to reach 25 billion by 2015 [3]. From the perspective of the information and communication technology, big data is a robust impetus to the next generation of information technology industries [4], which are broadly built on the third platform, mainly referring to big data, cloud computing, internet of things, and social business. Generally, Data warehouses have been used to manage the large dataset. In this case extracting the precise knowledge from the available big data is a foremost issue. Most of the presented approaches in data mining are not usually able to handle the large datasets successfully.
alysis of big data is the lack of coordination between database systems as well as with analysis tools such as data mining and statistical analysis. These challenges generally arise when we wish to perform knowledge discovery and ctical applications. A fundamental problem is how to quantitatively describe the essential characteristics of big data. There is a need for epistemological implications in describing data revolution [5]. Additionally, the study on data will help understand essential characteristics and formation of complex patterns in big data, simplify its representation, gets better knowledge abstraction, and guide the design of computing models and algorithms on big data [4].
Much research was carried out by various researchers on big data and its trends [6], [7], [8].
II. What is BIG DATA?  'Big Data' is similar to 'small data', but bigger in size  but having data bigger it requires different approaches: -Techniques, tools and architecture  an aim to solve new problems or old problems in a better way  Big Data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques.  Walmart handles more than 1 million customer transactions every hour.  Facebook handles 40 billion photos from its user base.  Decoding the human genome originally took 10years to process; now it can be achieved in one week.

III. Four Characteristics of Big Data V3s
1st Character of Big Data -Volume  A typical PC might have had 10 gigabytes of storage in 2000.  Today, Facebook ingests 500 terabytes of new data every day.  Boeing 737 will generate 240 terabytes of flight data during a single flight across the US.  The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantlyupdated data feeds containing environmental, location, and other information, including video.
2nd Character of Big Data -Velocity  Click streams and ad impressions capture user behaviour at millions of events per second  high-frequency stock trading algorithms reflect market changes within microseconds  machine to machine processes exchange data between billions of devices  infrastructure and sensors generate massive log data in real-time  Online gaming systems support millions of concurrent users, each producing multiple inputs per second.
3rd Character of Big Data -Variety  Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media.  Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure.  Big Data analysis includes different types of data 4 th Character of Big Data -Veracity  Refers to the biases, noise and abnormality in data.  Data that is being stored, and mined meaningful to the problem being analyzed.  Inderpal feel veracity in data analysis is the biggest challenge when compares to things like volume and velocity. million is in US itself  India will require a minimum of 1 lakh data scientists in the next couple of years in addition to data analysts and data managers to support the Big Data space.

Potential Value of Big Data
 $300 billion potential annual value to US health care.  $600 billion potential annual consumer surplus from using personal location data.  60% potential in retailers' operating margins.

Benefits of Big Data
 Real-time big data isn't just a process for storing pet bytes or exabytes of data in a data warehouse, It's about the ability to make better decisions and take meaningful actions at the right time.  Fast forward to the present and technologies like Hadoop give you the scale and flexibility to store data before you know how you are going to process it.  Technologies such as Map Reduce, Hive and Impala enable you to run queries without changing the data structures underneath.
 Our newest research finds that organizations are using big data to target customer-centric outcomes, tap into internal data and build a better information ecosystem.  Big Data is already an important part of the $64 billion database and data analytics market  It offers commercial opportunities of a comparable scale to enterprise software in the late 1980s  And the Internet boom of the 1990s, and the social media explosion of today.

Future of Big Data
 $15 billion on software firms only specializing in data management and analytics.  This industry on its own is worth more than $100 billion and growing at almost 10% a year which is roughly twice as fast as the software business as a whole.  In February 2012, the open source analyst firm Wikib on released the first market forecast for Big Data , listing $5.1B revenue in 2012 with growth to $53.4B in 2017  The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020.

VII. CONCLUSION
In recent years the amount of data has been increased drastically. To analyse those data became a challenging task for humans. In this paper, we discussed about big data and its various challenges and tools to analyse huge data. From the brief concepts, it is easily understood that every big data tool has specific functions. We belive that in future researchers will pay more attention to these techniques to solve problems of big data effectively and efficiently.