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Types Of Big Data

Big Data is creating a revolution in the IT field, every year the use of analytics is increasing drastically every year. We are creating 2.5 quintillion bytes of data every day hence the field is expanding in B2C apps. Big Data has entered almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the Globe. Let us first discuss- “What is Big Data?” “Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick google search will show. The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too large to be processed and analyzed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Facebook alone, in a single day- it represents a real problem in terms of analysis. Before we jump into the article, let's have a visual introduction on what is Big data and its types. (Structured Data, Semi-Structured & Unstructured Data) Types of Big Data: Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are- Structured Unstructured Semi-structured 1. Structured data Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities. There are two sources of structured data- machines and humans. All the data received from sensors, weblogs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few. Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications. Let’s understand Structured data with an example. Top 3 players who have scored most runs in international T20 matches are as follows: Player Country Scores No of Matches played                Brendon McCullum New Zealand                                 2140                                           71                    Rohit Sharma India     2237          90 Virat Kohli  India      2167          65 2. Unstructured data While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analyzing it manually. Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology. Human-generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content. This means that the pictures we upload to Facebook or Instagram handle, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data. Examples of unstructured data include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery – the list goes on and on. The following image will clearly help you to understand what exactly Unstructured data is The Unstructured data is further divided into – Captured User-Generated data a. Captured data: It is the data based on the user’s behavior. The best example to understand it is GPS via smartphones which help the user each and every moment and provides a real-time output. b. User-generated data: It is the kind of unstructured data where the user itself will put data on the internet every movement. For example, Tweets and Re-tweets, Likes, Shares, Comments, on Youtube, Facebook, etc. 3. Semi-structured data: The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily. Big Data analysis has been found to have definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity. Diagram showing Semi-structured data Difference between Structured, Semi-structured and Unstructured data       Factors      Structured data       Semi-structured data     Unstructured data Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. To minimize this talent gap many training institutes are offering courses on Big data analytics which helps you to upgrade skills set needed to manage and analyze big data. If you are keen to take up data analytics as a career then taking up Big data training will be an added advantage .
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Types Of Big Data

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Types Of Big Data

Big Data is creating a revolution in the IT field, every year the use of analytics is increasing drastically every year. We are creating 2.5 quintillion bytes of data every day hence the field is expanding in B2C apps. Big Data has entered almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the Globe.

Let us first discuss- “What is Big Data?”

“Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick google search will show.

The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too large to be processed and analyzed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Facebook alone, in a single day- it represents a real problem in terms of analysis.

Before we jump into the article, let's have a visual introduction on what is Big data and its types. (Structured Data, Semi-Structured & Unstructured Data)

Types of Big Data:

Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are-

Types of Big Data:

  • Structured
  • Unstructured
  • Semi-structured

1. Structured data

Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities.

There are two sources of structured data- machines and humans. All the data received from sensors, weblogs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few.

Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications.

Let’s understand Structured data with an example.

Top 3 players who have scored most runs in international T20 matches are as follows:

Structured data in Big Data Types

Player Country Scores No of Matches played               
Brendon McCullum New Zealand                                 2140                                           71                   
Rohit Sharma India     2237          90
Virat Kohli  India      2167          65


2. Unstructured data

While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analyzing it manually.

Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology.

Human-generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content. This means that the pictures we upload to Facebook or Instagram handle, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data.

Examples of unstructured data include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery – the list goes on and on.

The following image will clearly help you to understand what exactly Unstructured data is

Unstructured data in Big Data Types

The Unstructured data is further divided into –

  • Captured
  • User-Generated data

a. Captured data:

It is the data based on the user’s behavior. The best example to understand it is GPS via smartphones which help the user each and every moment and provides a real-time output.

b. User-generated data:

It is the kind of unstructured data where the user itself will put data on the internet every movement. For example, Tweets and Re-tweets, Likes, Shares, Comments, on Youtube, Facebook, etc.

3. Semi-structured data:

The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily.

Big Data analysis has been found to have definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity.

Diagram showing Semi-structured data

Semi-structured data in Big Data Types

Difference between Structured, Semi-structured and Unstructured data

      Factors      Structured data       Semi-structured data     Unstructured data
Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema
Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency
Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible
Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data

Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. To minimize this talent gap many training institutes are offering courses on Big data analytics which helps you to upgrade skills set needed to manage and analyze big data. If you are keen to take up data analytics as a career then taking up Big data training will be an added advantage
.

KnowledgeHut

KnowledgeHut

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KnowledgeHut is a fast growing Management Consulting and Training firm that is a source of Intelligent Information support for businesses and professionals across the globe.


Website : https://www.knowledgehut.com

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2 comments

Tony 20 Apr 2017

Hi, Thanks for sharing the information. These information will really help us a lot.

Rebheka 31 May 2018

This data is useful

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Netflix typically collects behavioral data and it then uses this data to create a better experience for the user.But Netflix uses Big Data for more than that, they monitor and analyze traffic details for various devices, spot problem areas and adjust network infrastructure to prepare for future demand. (later is action out of Big Data analytics, how big data analysis is put to use). They also try to get insights into types of content viewers to prefer and help them make informed decisions.   Apart from Netflix, Spotify is also a known great use case.2. Advertising and Media / Campaigning /EntertainmentFor decades marketers were forced to launch campaigns while blindly relying on gut instinct and hoping for the best. That all changed with digitization and big data world. 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Bang on, you would end up purchasing it as you anyway needed options best to choose from.3. Healthcare IndustryHealthcare is one of the classic use case industries for Big Data applications. The industry generates a huge amount of data.Patients medical history, past records, treatments given, available and latest medicines, Medicinal latest available research the list of raw data is endless.All this data can help give insights and Big Data can contribute to the industry in the following ways:Diagnosis time could be reduced, and exact requirement treatment could be started immediately. Most of the illnesses could be treated if a diagnosis is perfect and treatment can be started in time. This can be achieved through evidence-based past medical data available for similar treatments to doctor treating the illness, patients’ available history and feeding symptoms real-time into the system.  Government Health department can monitor if a bunch of people from geography reporting of similar symptoms, predictive measures could be taken in nearby locations to avoid outbreak as a cause for such illness could be the same.   The list is long, above were few representative examples.4. SecurityDue to social media outbreak, today, personal information is at stake. Almost everything is digital, and majority personal information is available in the public domain and hence privacy and security are major concerns with the rise in social media. Following are few such applications for big data.Cyber Crimes are common nowadays and big data can help to detect, predicting crimes.Threat analysis and detection could be done with big data.  5. Travel and TourismFlight booking sites, IRCTC track the clicks and hits along with IP address, login information, and other details and as per demand can do dynamic pricing for the flights/ trains. Big Data helps in dynamic pricing and mind you it’s real time. Am sure each one of us has experienced this. Now you know who is doing it :DTelecommunications, Public sector, Education, Social media and gaming, Energy and utility every industry have implemented are implementing several of these Big Data use cases day in and day out. If you look around am sure you would find them on the rise.Big Data is helping everyone industries, consumers, clients to make informed decisions, whatever it may be and hence wherever there is such a need, Big Data can come handy.Challenges faced by Big Data in the real world for adaptationAlthough the world is going gaga about big data, there are still a few challenges to implement and adopt Big Data and hence service industries are still striving towards resolving those challenges to implement best Big Data solution without flaws.An October 2016 report from Gartner found that organizations were getting stuck at the pilot stage of their big data initiatives. "Only 15 percent of businesses reported deploying their big data project to production, effectively unchanged from last year (14 per cent)," the firm said.Let’s discuss a few of them to understand what are they?1. Understanding Big Data and answering Why for the organization one is working with.As I started the article saying there are many versions of Big Data and understanding real use cases for organization decision makers are working with is still a challenge. Everyone wants to ride on a wave but not knowing the right path is still a struggle. As every organization is unique thus its utmost important to answer ‘why big data’ for each organization. This remains a major challenge for decision makers to adapt to big data.2. Understanding Data sources for the organizationIn today’s world, there are hundreds and thousands of ways information is being generated and being aware of all these sources and ingest all of them into big data platforms to get accurate insight is essential. Identifying sources is a challenge to address.It's no surprise, then, that the IDG report found, "Managing unstructured data is growing as a challenge – rising from 31 per cent in 2015 to 45 per cent in 2016."Different tools and technologies are on the rise to address this challenge.3. Shortage if Big Data Talent and retaining themBig Data is changing technology and there are a whopping number of tools in the Big Data technology landscape. It is demanded out of Big Data professionals to excel in those current tools and keep up self to ever-changing needs. This gets difficult for employees and employers to create and retain talent within the organization.The solution to this would be constant upskilling, re-skilling and cross-skilling and increasing budget of organization for retaining talent and help them train.4. The Veracity VThis V is a challenge as this V means inconsistent, incomplete data processing. To gain insights through big data model, the biggest step is to predict and fill missing information.This is a tricky part as filling missing information can lead to decreasing accuracy of insights/ analytics etc.To address this concern, there is a bunch of tools. Data curation is an important step in big data and should have a proper model. But also, to keep in mind that Big Data is never 100% accurate and one must deal with it.5. SecurityThis aspect is given low priority during the design and build phases of Big Data implementations and security loopholes can cost an organization and hence it’s essential to put security first while designing and developing Big Data solutions. Also, equally important to act responsibly for implementations for regulatory requirements like GDPR.  6. Gaining Valuable InsightsMachine learning data models go through multiple iterations to conclude on insights as they also face issues like missing data and hence the accuracy. To increase accuracy, lots of re-processing is required, which has its own lifecycle. Increasing accuracy of insights is a challenge and which relates to missing data piece. Which most likely can be addressed by addressing missing data challenge.This can also be caused due to unavailability of information from all data sources. Incomplete information would lead to incomplete insights which may not benefit to required potential.Addressing these discussed challenges would help to gain valuable insights through available solutions.With Big Data, the opportunities are endless. Once understood, the world is yours!!!!Also, now that you understand BIG DATA, it's worth understanding the next steps:Gary King, who is a professor at Harvard says “Big data is not about the data. It is about the analytics”You can also take up Big Data and Hadoop training to enhance your skills furthermore.Did the article helps you to understand today’s massive world of big data and getting a sneak peek into it Do let us know through the comment section below?
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What is Big Data — An Introductory Guide

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Apache Spark Vs Apache Storm - Head To Head Comparison

In today’s world, the need for real-time data streaming is growing exponentially due to the increase in real-time data. With streaming technologies leading the world of Big Data, it might be tough for the users to choose the appropriate real-time streaming platform. Two of the most popular real-time technologies that might consider for opting are Apache Spark and Apache Storm. One major key difference between the frameworks Spark and Storm is that Spark performs Data-Parallel computations, whereas Storm occupies Task-Parallel computations. Read along to know more differences between Apache Spark and Apache Storm, and understand which one is better to adopt on the basis of different features. Comparison Table: Apache Spark Vs. Apache StormSr. NoParameterApache SparkApache Storm1.Processing  ModelBatch ProcessingMicro-batch processing2.Programming LanguageSupports lesser languages like Java, Scala.Support smultiple languages, such as Scala, Java, Clojure.3.Stream SourcesHDFSSpout4.MessagingAkka, NettyZeroMQ, Netty5.Resource ManagementYarn and Meson are responsible.Yarn and Mesos are responsible.6.Low LatencyHigher latency as compared to SparkBetter latency with lesser constraints7.Stream PrimitivesDStreamTuple, Partition8.Development CostSame code can be used for batch and stream processing.Same code cannot be used for batch and stream processing.9.State ManagementSupports State ManagementSupports State Management as well10.Message Delivery GuaranteesSupports one message processing mode: ‘at least once’.Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.11.Fault ToleranceIf a process fails, Spark restarts workers via resource managers. (YARN, Mesos)If a process fails, the supervisor process starts automatically.12.Throughput100k records per node per second10k records per node per second13.PersistenceMapStatePer RDD14.ProvisioningBasic monitoring using GangliaApache AmbariApache Spark: Apache Spark is a general-purpose, lighting fast, cluster-computing technology framework, used for fast computation on large-scale data processing. It can manage both batch and real-time analytics and data processing workloads.  Spark was developed at UC Berkeley in the year 2009. Apache Storm:Apache Storm is an open-source, scalable fault-tolerant, and real-time stream processing computation system. It is a framework for real-time distributed data processing, which focuses on stream processing or event processing. It can be used with any programming language and can be integrated using any queuing or database technology.  Apache Storm was developed by a team led by Nathan Marz at BackType Labs. Apache Spark Vs. Apache Storm1. Processing Model: Apache Storm supports micro-batch processing, while Apache Spark supports batch processing. 2. Programming Language:Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.3. Stream Sources:For Storm, the source of stream processing is Spout, while that for Spark is HDFS. 4. Messaging:Storm uses ZeroMQ and Netty as its messaging layer while Spark is using a combination of Nettu and Akka for distributing the messages throughout the executors. 5. Resource Management:Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm. 6. Low Latency: Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.7. Stream Primitives:Spark provides with stream transforming operators which transform DStream into another, while Storm provides with various primitives which perform tuple level of processing at the stream level (functions, filters). 8. Development Cost:It is possible for Spark to use the same code base for both stream processing and batch processing. Whereas for Storm, the same code base cannot be used for both stream processing and batch processing.  9. State Management: The changing and maintaining state in Apache Spark can be updated via UpdateStateByKey, but no pluggable strategy can be applied in the external system for the implementation of state. Whereas Storm does not provide any framework for the storage of any intervening bolt output as a state. Hence, each application has to create a state for itself whenever required. 10. Message Delivery Guarantees (Handling the message level failures):Apache Spark supports only one message processing mode, viz, ‘at least once’, whereas Storm supports three message processing modes, viz, ‘at least once’ (Tuples are processed at least one time, but can be processed more than once), ‘at most once’  and ‘exactly once’ (T^uples are processed at least once). Storm’s reliability mechanisms are scalable, distributed and fault-tolerant. 11. Fault-Tolerant:Apache Spark and Apache Storm, both are fault tolerant to nearly the same extent. If a process fails in Apache Storm, then the supervisor process will restart it automatically, as the state management is managed by Zookeeper, while Spark restarts its workers with the help of resource managers, who may be Mesos, YARN or its separate manager.12. Ease of Development: In the case of Storm, there are effective and easy to use APIs which show that the nature of topology is DAG. The Storm tuples are written dynamically. In the case of Spark, it consists of Java and Scala APIs with practical programming, making topology code a bit difficult to understand. But since the API documentation and samples are easily available for the developers, it is now easier. Summing Up: Apache Spark Vs Apache StormApache Storm and Apache Spark both offer great solutions to solve the transformation problems and streaming ingestions. Moreover, both can be a part of a Hadoop cluster to process data. While Storm acts as a solution for real-time stream processing, developers might find it to be quite complex to develop applications due to its limited resources. The industry is always on a lookout for a generalized solution, which has the ability to solve all types of problems, such as Batch processing, interactive processing, iterative processing and stream processing. Keeping all these points in mind, this is where Apache Spark steals the limelight as it is mostly considered as a general-purpose computation engine, making it a highly demanding tool by IT professionals. It can handle various types of problems and provides a flexible environment to in. Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 
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Apache Spark Vs Apache Storm - Head To Head Compar...

In today’s world, the need for real-time data st... Read More

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