Big Data technology has seen a rapid growth in recent years. Big Data tools like Hadoop etc are extensively used in various fields. This post will discuss it, its functionalities, categories, attributes, applications and advantages as well as disadvantages.
What is Big Data
Dataset that are highly intricate and is beyond the storage capacity and processing power of the computer is called Big Data.
These are exceedingly huge datasets with proportions beyond the ability of day-to-day computable activities that will eventually end up using software tools to capture, analyze, share, transfer, manage & process the dataset.
Function Mechanism of BigData
BigData helps in jet setting real-time computing decisions that estimate in assessing an out flux of facts and figures from social media, logistics, financial, retailer databases.
It succors in understanding the past, predicting the future, detecting patterns in datasets.
Categories of Big Data
The umbrella of ‘Big Data’ houses three groups, mainly:
It is the defined size of dataset which is precise and highly efficient. This is the most systematic dataset model because here any dataset can be stockpiled, obtained, organized, recouped and maneuvered in any way. This type of dataset resides in relational database and helps in easy storage.
Example: Dataset warehouses, Enterprise systems, Databases
It is the type of dataset that cannot be well ordered and customarily does not have any structured row-column configuration. Big data software tools like Hadoop can undertake the activity to organize and manage such disassembled dataset that are extremely convoluted, acutely huge and change rapidly.
Example: Text documents, Audio/video streams, log files
It is a self-describing dataset where the dataset format is implied and deducible. In this kind of structure, not necessarily all the acquired statistics may be similar and the schema can differ within a single database and over a period of time it can fluctuate imperiously.
Example: HTML, XML, RDF
Attributes of Big Data
The attributes of BigData are as follows:
Fig. 2 – Attributes of BigData
Volume of Dataset
- Recorded & transacted dataset amounting to the time consumed.
- Scaling of the bulky dataset.
Example: High resolution sensors
Velocity of Dataset
- Speed at which the dataset is originated.
- Processing and analysing of the streaming dataset.
Example: Improved connectivity
Variety of Data
- Different forms of dataset.
- Heterogeneous & noisy dataset.
Example: Structured-Data, Unstructured-Data, Semi-structured Data
Veracity of Data
- Incoming dataset from unreliable resources.
- Inaccuracy of the dataset.
Example: Costing, Source availability issues
Value of Data
- Scientifically related dataset.
- Elongated studies
Example: Simulation, Hypothetical events
Applications of Big Data
Fig. 3 – Applications of BigData
The applications of Big Data in various fields are as follows: –
In Health / Life Science
- Unearthing new medicines & developing it further.
- Analysis of disease patterns
In Retail /Consumer
- Managing supply-chains
- Targeting events
- Customer based programs
- Marketing segments
In Digital Media
- Controlling campaigns
- Targeting advertisements
- Click fraud prevention
In Finance Services
- Management of risk analysis
- Detecting fraud services
- Compliance & regulating the issues
- Propagating proper offers at the proper time
- Highly directed efficient engines that use predictive analytics
Advantages of BigData
Its advantages are as follows: –
- Extracts ingenious results and helps in establishing main causes that hinder real-time issues.
- It is the biggest software boom because it intensifies cyber surveillance.
- BigData is the next big thing as it helps in upgrading the sector of health care and has given a way for deeper understanding in the analysis of digital forensics.
- Since it is an open source, it has pathways to large information via surveys and add-ons happen every other second.
- Provides flexibility in financial markets and enhances sports consummation.
Disadvantages of BigData
Its disadvantages are as follows: –
- There will be breach in the confidentiality of certain criterion in ‘BigData’.
- To keep up with the refurbishes BigData needs lot of agility to harmonize the data.
- It is always not an accommodating environment for analysts, data-mining connoisseurs as the conversion of progressive dataset to analysis of the same dataset sometimes proves to be a uphill task.
- It is not useful for short run and sometimes strenuous to handle such Big Data.
- There are always technical and analytical challenges.
Fig. 4 – BigData Hadoop Tool
Big Data Hadoop Tool
The emerging environment of ‘Big Data’ has Hadoop as its intermediary crux to support all of its primary activities. It is an easily accessible informant where this software framework is used in machine learning applications, predictive analytics, data-mining etc. This is a distinguished framework where the dominant usage is for batch processing.
The Apache Hadoop is a famous open-source software utility that simplifies a cluster of network from distinct computers to resolve mammoth amount of dataset.
Components of BigData Hadoop Tool
The important components of BigData Hadoop Tool are:
- Hadoop distributed file system (HDFS)
- Hadoop YARN
- Hadoop MapReduce
Hadoop distributed file system (HDFS)
Hadoop distributed file system (HDFS) is used for storage of the dataset. It has a master/slave architecture that sets up an error tolerant planning.
Hadoop YARN (Yet Another Resource Negotiator)
Hadoop YARN is used for blob management of dataset and is used to separate HDFS and MapReduce. It is used for dynamic allocation of lagoon of dataset from resource point to application point.
Hadoop MapReduce is used in the development of the dataset and to learn the measure and mechanism of the dataset. It is used for static allocation of dataset of resources for designated tasks.