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Introduction of Big Data
Big data generally refers to all types of data which are generated all over the world at an unprecedented rate. Big Data can be of two types-structured or unstructured. There is large need to convert Big Data into Business Intelligence. It not only leads to decision making scenario but also enhance strategies for organizations irrespective of its size, share segmentation and various other factors. For working on a large volume of data platform like Hadoop is the perfect choice.
Big Data has different features and are explained using 4vs which are-
- Volume:-Collection of giant data and plays important role in Big Data Analytics.
- Variety:-Data collected are categorized in different ways like video, text, numeric etc.and hence big data plays an important role to unlock its value.
- Veracity:-It clarifies whether the available data is coming from a credible source which is also one of the important factors for Business needs.
- Velocity:-It tells about the rate at which new data is produced.
In other words, it is defined as Big data is a collection of data with large amount of size. It is a category of computing strategies and technologies which are used to handle extreme number of datasets.
Some Examples of Big data are-
- The New York Stock Exchange produces data which is about 1 terabyte per day.
- Social Media-Data Statics shows that in social sites like Facebook 500+ terabytes of information are generated in a single day which includes like messages, videos, comments and various other things.
- Jet Engine can generate about 10+ terabytes of data in about half an hour of time.
Classification of Big Data
- Structured:-Data which is stored in a fixed format is known as structured data.
- Unstructured:-Data which has no form or which has no definite structure is known as unstructured data. Its Example like storing heterogeneous data source which consists of files, images, videos etc. The best Example may be “google search”.
Evolution of Big Data
About 90% of the data is evolved in the past 2 years and the term “Big Data” has emerged near about 2005 which was launched by O’Reilly Media in 2005.But the usage of Big Data was available from the longer times.
It was estimated that the earliest records of data were obtained from 7000 years ago in Mesopotamia, where accounting was introduced to handle the stats of growth of crops and herds.
Later on, this accounting principles later on improved further till 1663 and during this year, John Graunt examined and he recorded details regarding mortality roles in London. In his first recorded statistical data he gathered his findings which emerged as a great insight to the cause of death in the 17th century.
From there onwards, the information age started. It was in 1887 that Herman Hollerith invented a machine which can read the holes which are punched onto the paper cards in order to get census data. Later on, the first data processing machine was developed in 1943 during world war-II. In 1965 United State Government takes the decision to establish 1st data center to store tax return which is about 742 million tax returns and about 175 million sets of fingerprints which is stored in a single location. The project was not further carried out but it was the beginning of data storage era.
In the past few years, there are lots are organizations are working to handle big data and many companies are heading towards big Data. Big Data is ahead of us and many more things will change in the upcoming years.
Advantages of Big Data:
Big Data Algorithms
- Time Reductions:-Hadoop helps in analyzing the business data in a quicker way and also makes in quick decisions based on the learning.
- New Product Development:-Looking into the requirements of the customers, data analytics helps in designing new products.
- Cost Savings:-Some working tools like Hadoop can be a boon to the business where large amount of information is stored and can be accessed in an easy way.
There are several Algorithms to extract the values from the data. Few of the major algorithms are mentioned below-
- Linear Regression:-It is one of important fundamental algorithms of data analytics which makes it one of the largest used algorithms. It is quite simpler and people can visualize the working mechanisms and interlink the input and output data processing. It uses the relationships between the 2 sets of quantitative measures called independent variable as the first and the dependent as the second.
The major objective of linear regression is to create the relationships among the dependent variable with the independent variable. Once the relationship is formulated, the dependent variable can be predicted for any independent variable.
Linear Regression is good for predicting the trends and to display the effects of new policy. It is used particularly when the outcome is a score than category.
- Logistics Regression:-Although it sounds very much similar to linear regression but it focuses on categorization rather than quantitative forecasting. In Logistics regression the outcome variables are discrete in values and finite rather than infinite and continuous as it was in linear regression.
The major objective of the logistic regression is that is to make separate whether the instance of input variable fits exactly inside the category or not.
- Classification and Regression Trees:-It makes use of decisions to categorize the data. Every conclusion is based on input variables which is based on a question. For every question with its respective response the instance of data becomes small or closer to categorize in a certain way. As branching is increased the classification becomes quite complicates and large. To handle this complexity the better way is to remove levels of questions to balance between abstraction and exact fit. Variant of regression and classification is known as random forests.
- K-Nearest Neighbours:-It is also known as “lazy learner” because of its fixed training phase. It can be expensive mechanism as it depends on scope and size of the training set. In this mechanism every instance is compared to the instances of all the training data sets, so this operation can utilize various computing resources every time it is processed. It is generally used in search applications when common product searching is the initial priority.It is simple and easy to train and get the output.
- K-Means Clustering :-It generally concentrates on making groups of same attributes which is called as clusters. Once the cluster is created other instances is compared with this clusters and analyze whether they best fit or not. It is mainly used as a part of data exploration.
- Association Rule Mining :-Association Rule Mining is also known as “Market Basket Analysis” because this implemented the algorithm. It can recognize that associations which is impossible to identify in a random sampling.
Applications and Uses of Big Data
Big Data is playing its important role in different domains.
- Health care:-Earlier the medical industry failed in using Big Data because they do not have the capacity to standardize and consolidate data. But with the evolution of Big Data, healthcare has been improved by giving prescriptive analytics and personalized medicines.
- Manufacturing:-Manufacturing needs large amounts of data and very advanced prediction tools to change data into useful information. Main advantages of using Big Data are in supply planning, output forecasting, increasing energy efficiency and several other manufacturing industries.
- Weather Patterns:-All around the earth there are many weather sensors and satellites which gives large amounts of data and weather conditions is monitored by this data. All the information from the sensors can be used in various situation as in weather forecasting, to study about global warming, helps to know the patterns of natural disasters as well as it also helps to make ready for emergency crises.
- Government Sector:-Governments holds large amounts of data and keep record of different records and databases about their citizens, energy resources and lot more things. The analysis of data is used to help the government in various ways like in welfare schemes, Cyber security etc.
- Media and Entertainment:-Big data helps in gives large amounts of details for millions of individuals. Big Data applications are boosting media and entertainment by predicting the audience desire, targeting the Ads, for product development and content development, getting insights from customer reviews.
- Insurance:-Big Data allows customer retention from insurance companies. When claiming the management is taken into account, predictive analysis from big data is used for fast service and also fraud detection can be enhanced.
- SmartPhones:-Facial recognition is possible because of the use of big data. Users of iPhone or Android makes use of fingerprints that use facial technology for numerous tasks.
- Transportation:-Big Data regarding transportation is used by Governments, private organizations and also by the individuals.Governments uses big data in controlling the traffic, in planning the route, and also in the congestion management.
Private Organizations uses the Big data in revenue Management, logistics and also for their competitive advantages. Individuals also makes use of big data regarding planning to save fuel and time, for tourism and various other works.
- Banking Sector:-The data stored in banks are gradually increasing at an alarming rate. Big Data helps to study and analyze many illegal activities like fraud use of credit r debit cards, money laundering, customer statistics alteration etc. Various software’s like SAS AML use Data Analytics which is used to detect the fraud transactions and helps to analyze the customer data.
- Agriculture:-Sensor data is used by the biotechnology firm to increase the crop efficiency. It data environment fits accordingly to temperature, soil composition and gene sequencing of each plant.
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