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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 .

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

Author

KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
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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Overview of Deploying Machine Learning Models

Machine Learning is no longer just the latest buzzword. In fact, it has permeated every facet of our everyday lives. Most of the applications across the world are built using Machine Learning and their applications extend further when they are combined with other cutting-edge technologies like Deep Learning and Artificial Intelligence. These latest technologies are a boon to mankind, as they simplify tasks, helping to complete work in lesser time. They boost the growth and profitability of industries and organizations across sectors, which in turn helps in the growth of the economy and generates jobs.What are the fields that machine learning extends into?Machine Learning now finds applications across sectors and industries including fields like Healthcare, defense, insurance, government sectors, automobile, manufacturing, retail and more. ML gives great insights to businesses in gaining and retaining customer loyalty, enhances efficiency, minimizes the time consumption, optimizes resource allocation and decreases the cost of labor for a specific task.What is Model Deployment?It’s well established that ML has a lot of applications in the real world. But how exactly do these models work to solve our problems? And how can it be made available for a large user base? The answer is that we have to deploy the trained machine learning model into the web, so that it can be available for many users.When a model is deployed, it is fully equipped with training and it knows what are the inputs to be taken by the model and what are the outputs given out in return. This strategy is used to advantage in real world applications. Deployment is a tricky task and is the last stage of our ML project.Generally, the deployment will take place on a web server or a cloud for further use, and we can modify the content based on the user requirements and update the model. Deployment makes it easier to interact with the applications and share the benefits to the applications with others.With the process of Model Deployment, we can overcome problems like Portability, which means shifting of software from one machine to the other and Scalability, which is the capacity to be changed on a scale and the ability of the computation process to be used in a wide range of capabilities.Installing Flask on your MachineFlask is a web application framework in Python. It is a lightweight Web Server Gateway Interface (WSGI) web framework. It consists of many modules, and contains different types of tools and libraries which helps a web developer to write and implement many useful web applications.Installing Flask on our machine is simple. But before that, please ensure you have installed Python in your system because Flask runs using Python.In Windows: Open command prompt and write the following code:a) Initially make the virtual environment using pip -- pip install virtualenv And then write mkvirtualenv HelloWorldb) Connect to the project – Create a folder dev, then mkdir Helloworld for creating a directory; then type in cd HelloWorld to go the file location.c) Set Project Directory – Use setprojectdir in order to connect our virtual environment to the current working directory. Now further when we activate the environment, we will directly move into this directory.d) Deactivate – On using the command called deactivate, the virtual environment of hello world present in parenthesis will disappear, and we can activate our process directly in later steps.e) Workon – When we have some work to do with the project, we write the command  “workon HelloWorld” to activate the virtual environment directly in the command prompt.The above is the set of Virtual Environment commands for running our programs in Flask. This virtual environment helps and makes the work easier as it doesn’t disturb the normal environment of the system. The actions we perform will reside in the created virtual environment and facilitate the users with better features.f) Flask Installation – Now you install flask on the virtual environment using command pip install flaskUnderstanding the Problem StatementFor example, let us try a Face Recognition problem using opencv. Here, we work on haarcascades dataset. Our goal is to detect the eyes and face using opencv. We have an xml file that contains the values of face and eyes that were stored. This xml file will help us to identify the face and eyes when we look into the camera.The xml data for face recognition is available online, and we can try this project on our own after reading this blog. For every problem that we solve using Machine Learning, we require a dataset, which is the basic building block for the Model development in ML. You can generate interesting outcomes at the end like detecting the face and eyes with a bounding rectangular box. Machine learning beginners can use these examples and create a mini project which will help them to know much about the core of ML and other technologies associated with it.Workflow of the ProjectModel Building: We build a Machine Learning model to detect the face of the human present in front of the camera. We use the technology of Opencv to perform this action which is the library of Computer Vision.Here our focus is to understand how the model is working and how it is deployed on server using Flask. Accuracy is not the main objective, but we will learn how the developed ML model is deployed.Face app: We will create a face app that detects your face and implements the model application. This establishes the connection between Python script and the webpage template.Camera.py: This is the Python script file where we import the necessary libraries and datasets required for our model and we write the actual logic for the model to exhibit its functionality.Webpage Template: Here, we will design a user interface where the user can experience live detection of his face and eyes in the camera. We provide a button on a webpage, to experience the results.Getting the output screen: when the user clicks the button, the camera will open directly and we can get the result of the machine learning model deployed on the server. In the output screen you can see your face. Storage: This section is totally optional for users, and it is based on the users’ choice of storing and maintaining the data. After getting the outputs on the webpage screen, you can store the outputs in a folder on your computer. This helps us to see how the images are captured and stored locally in our system. You can add a file path in the code, that can store the images locally on your system if necessary.This application can be further extended to a major project of “Attendance taking using Face Recognition Technique”, which can be used in colleges and schools, and can potentially replace normal handwritten Attendance logs. This is an example of a smart application that can be used to make our work simple.Diagrammatic Representation of the steps for the projectBuilding our Machine Learning ModelWe have the XML data for recognizing face and eyes respectively. Now we will write the machine learning code, that implements the technique of face and eyes detection using opencv. Before that, we import some necessary libraries required for our project, in the file named camera.py # import cv2 # import numpy as np # import scipy.ndimage # import pyzbar.pyzbar as pyzbar # from PIL import Image Now, we load the dataset into some variables in order to access them further. Haarcascades is the file name where all the xml files containing the values of face, eye, nose etc are stored. # defining face detector# face_cascade = cv2.CascadeClassifier("haarcascades/haarcascade_frontalface_default.xml") # eye_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_eye.xml')The xml data required for our project is represented as shown below, and mostly consists of numbers.Now we write the code for opening the camera, and releasing of camera in a class file. The “def” keyword is the name of the function in Python. The functions in Python are declared using the keyword “def”.The function named “def __init__” initiates the task of opening camera for live streaming of the video. The “def __del__” function closes the camera upon termination of the window.# class VideoCamera(object):#    def __init__(self):        # capturing video#       self.video = cv2.VideoCapture(0) #  def __del__(self):#        # releasing camera#        self.video.release()Next, we build up the actual logic for face and eyes detect using opencv in Python script as follows. This function is a part of class named videocamera.# class VideoCamera(object):#    def __init__(self):#        # capturing video#        self.video = cv2.VideoCapture(0)#    def __del__(self):#        # releasing camera#        self.video.release()#    def face_eyes_detect(self):#        ret, frame = self.video.read()#        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)#        faces = face_cascade.detectMultiScale(gray, 1.3, 5)#        c=0#        for (x,y,w,h) in faces:#            cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0), 2)#            roi_gray = gray[y:y+h, x:x+w]#            roi_color = frame[y:y+h, x:x+w]#            eyes = eye_cascade.detectMultiScale(roi_gray)#            for (ex,ey,ew,eh) in eyes:#                cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)#            while True:#                k = cv2.waitKey(1000) & 0xFF#                print("Image "+str(c)+" saved")#                file = 'C:/Users/user/dev/HelloWorld/images/'+str(c)+'.jpg'#                cv2.imwrite(file, frame)#                c += 1            # encode Opencv raw frame to jpg and display it#        ret, jpeg = cv2.imencode('.jpg', frame)#        return jpeg.tobytes()The first line in the function “ret, frame” reads the data of live streaming video. The ret takes the value “1”, when the camera is open, else it takes “0” as input. The frame captures the live streaming video from time to time. In the 2nd line, we are changing the color of image from RGB to Grayscale, i.e., we are changing the values of pixels. And then we are applying some inbuilt functions to detect faces. The for loop, illustrates that it is having some fixed dimensions to draw a bounding rectangular box around the face and eyes, when it is detected. If you want to store the captured images after detecting face and eyes, we can add the code of while loop, and we can give the location to store the captured images. When an image is captured, it is saved as Image 1, Image 2 saved, etc., for confirmation.All the images will be saved in jpg format. We can mention the type of format in which the images should be stored. The method named cv2.imwrite stores the frame in a particular file location.Finally, after capturing the detected picture of face and eyes, it displays the result at the user end. Creating a WebpageWe will create a webpage, in order to implement the functionality of the developed machine learning model after deployment using Flask. Here is the design of our webpage.The above picture represents a small webpage demonstrating “Video Streaming Demonstration” and a link “face-eyes-detect”. When we click the button on the screen, the camera gets opened and the functionality will be displayed to the users who are facing the camera.The code for creating a webpage is as follows:If the project contains only one single html file, it should be necessarily saved with the name of index. The above code should be saved as “index.html” in a folder named “templates” in the project folder named “HelloWorld”, that we have created in the virtual environment earlier. This is the actual format we need to follow while designing a webpage using Flask framework.Connecting Webpage to our ModelTill now we have developed two separate files, one for developing the machine learning model for the problem statement and the other for creating a webpage, where we can access the functionality of the model. Now we will try to see how we can connect both of them.This is the Python script with the file name saved as “app.py”. Initially we import the necessary libraries to it, and create a variable that stores the Flask app. We then guide the code to which location it needs to be redirected, when the Python scripts are executed in our system. The redirection is done through “@app.route” followed by a function named “home”. Then we include the functionality of model named “face_eyes_detect” to the camera followed by the function definition named “gen”. After adding the functionality, we display the response of the deployed model on to the web browser. The outcome of the functionality is the detection of face and eyes in the live streaming camera and the frames are stored in the folder named images. We put the debug mode to False. # from flask import Flask, render_template, Response,url_for, redirect, request.# from flask import Flask, render_template, Response,url_for, redirect, request  # from camera import VideoCamera  # import cv2  # import time  # app = Flask(__name__)  # @app.route("/")  # def home():  #     # rendering web page  #     return render_template('index.html')  # def gen(camera):  #     while True:  #         # get camera frame  #         frame = camera.face_eyes_detect()  #         yield(b'--frame\r\n'  #                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')  # @app.route("/video_feed")  # def video_feed():  #     return Response(gen(VideoCamera()),  #           mimetype='multipart/x-mixed-replace; boundary=frame')  # if __name__ == '__main__':  #     # defining server ip address and port  #     app.run(debug=False)Before running the Python scripts, we need to install the libraries like opencv, flask, scipy, numpy, PIL, pyzbar etc., using the command prompt with the command named “pip install library_name” like “pip install opencv-python”, ”pip install flask”, “pip install scipy” etc.When you have installed all the libraries in your system, now open the python script “app.py” and run it using the command “f5”. The output is as follows:Image: Output obtained when we run app.py fileNow we need to copy the server address http://127.0.0.1:5000/ and paste it on the web browser, and we will get the output screen as follows:Now when we click the link “face-eyes-detect”, we will get the functionality of detecting the face and eyes of a user, and it is seen as follows:Without SpectaclesWith SpectaclesOne eye closed by handone eye closedWhen these detected frames are generated, they are similarly stored in a specified location of folder named “images”. And in the Python shell we can observe, the sequence of images is saved in the folder, and looks as follows:In the above format, we get the outcomes of images stored in our folder.Now we will see how the images were stored in the previously created folder named “images” present in the project folder of “HelloWorld.”Now we can use the deployed model in real time. With the help of this application, we can try some other new applications of Opencv and we can deploy it in the flask server accordingly.  You can find all the above code with the files in the following github repository, and you can make further changes to extend this project application to some other level.Github Link.ConclusionIn this blog, we learnt how to deploy a model using flask server and how to connect the Machine Learning Model with the Webpage using Flask. The example project of face-eyes detection using opencv is a pretty common application in the present world. Deployment using flask is easy and simple.  We can use the Flask Framework for deployment of ML models as it is a light weight framework. In the real-world scenario, Flask may not be the most suitable framework for bigger applications as it is a minimalist framework and works well only for lighter applications.
Overview of Deploying Machine Learning Models
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The use of Big Data over a network cluster has become a major application in multiple industries. The wide use of MapReduce and Hadoop technologies is proof of this evolving technology, along with the recent rise of Apache Spark, a data processing engine written in Scala programming language. Introduction to Scala Scala is a general purpose object-oriented programming language, similar to Java programming. Scala is an acronym for “Scalable language” meaning its capabilities can grow along the lines of your requirements & also there are more technologies built on scala. The capabilities of Scala programming can range from a simple scripting language to the preferred language for mission-critical applications. Scala has the following capabilities: Support for functional programming, with features including currying, type interference, immutability, lazy evaluation, and pattern matching. An advanced type system including algebraic data types and anonymous types. Features that are not available in Java, like operator overloading, named parameters, raw strings, and no checked exceptions. Scala can run seamlessly on a Java Virtual Machine (JVM), and Scala and Java classes can be freely interchanged or can refer to each other. Scala also supports cluster computing, with the most popular framework solution, Spark, which was written using Scala. Introduction to Apache Spark Apache Spark is an open-source Big Data processing framework that provides an interface for programming data clusters using data parallelism and fault tolerance. Apache Spark is widely used for fast processing of large datasets. Apache Spark is an open-source platform, built by a wide set of software developers from over 200 companies. Since 2009, more than 1000 developers have contributed to Apache Spark. Apache Spark provides better capabilities for Big Data applications, as compared to other Big Data technologies such as Hadoop or MapReduce. Listed below are some features of Apache Spark: 1. Comprehensive framework Spark provides a comprehensive and unified framework to manage Big Data processing, and supports a diverse range of data sets including text data, graphical data, batch data, and real-time streaming data. 2. Speed Spark can run programs up to 100 times faster than Hadoop clusters in memory, and 10 times faster when running on disk. Spark has an advanced DAG (directed acrylic graph) execution engine that provides support for cyclic data flow and in-memory data sharing across DAGs to execute different jobs with the same data. 3. Easy to use With a built-in set of over 80 high-level operators, Spark allows programmers to write Java, Scala, or Python applications in quick time. 4. Enhanced support In addition to Map and Reduce operations, Spark provides support for SQL queries, streaming data, machine learning, and graphic data processing. 5. Can be run on any platform Apache Spark applications can be run on a standalone cluster mode or in the cloud. Spark provides access to diverse data structures including HDFS, Cassandra, HBase, Hive, Tachyon, and any Hadoop data source. Spark can be deployed as a standalone server or on a distributed framework such as Mesos or YARN. 6. Flexibility In addition to Scala programming language, programmers can use Java, Python, Clojure, and R to build applications using Spark. Comprehensive library support As a Spark programmer, you can combine additional libraries within the same application, and provide Big Data analytical and Machine learning capabilities. The supported libraries include: Spark Streaming, used for processing of real-time streaming data. Spark SQL, used for exposing Spark datasets over JDBC APIs and for executing SQL-like queries on Spark datasets. Spark MLib, which is the machine learning library, consisting of common algorithms and utilities. Spark GraphX, which is the Spark API for graphs and graphical computation . BlinkDB, a query engine library used for running interactive SQL queries on large data volumes. Tachyon, which is a memory-centric distributed file system to enable file sharing across cluster frameworks. Spark Cassandra Connector and Spark R, which are integration adapters. With Cassandra Connector, Spark can access data from the Cassandra database and perform data analytics. Compatibility with Hadoop and MapReduce Apache Spark can be much faster as compared to other Big Data technologies. Apache Spark can run on an existing Hadoop Distributed File System (HDFS) to provide compatibility along with enhanced functionality. It is easy to deploy Spark applications on existing Hadoop v1 and v2 cluster. Spark uses the HDFS for data storage, and can work with Hadoop-compatible data sources including HBase and Cassandra. Apache Spark is compatible with MapReduce and enhances its capabilities with features such as in-memory data storage and real-time processing. Conclusion The standard API set of Apache Spark framework makes it the right choice for Big Data processing and data analytics. For client installation setups of MapReduce implementation with Hadoop, Spark and MapReduce can be used together for better results. Apache Spark is the right alternative to MapReduce for installations that involve large amounts of data that require low latency processing
Analysis Of Big Data Using Spark And Scala
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Analysis Of Big Data Using Spark And Scala

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