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Who Can Do Hadoop course?

Hadoop is a Java-based programming framework which is open source. This framework is useful in processing and storage of huge data sets in a distributed computing environment. It handles thousands of terabytes of data and runs applications on systems with thousands of hardware nodes. The data transfer rates among the nodes are very high and even allows the system to continue working in case a node fails. Hadoop admin training lets you understand the Hadoop framework, HDFS and every related technology. It has four industry-based projects and is suited to data engineers, IT professionals, cloud administrators and system administrators. There will be different pros & cons in hadoop and more details about the course are given towards the end of the article. Who is eligible to do Hadoop course? Planning to learn Hadoop is a good decision if you are working in the Information Technology industry. There are no specific prerequisites to start learning the framework. But, it is recommended to know the basics of Java and Linux if you want to become a Hadoop expert and opt for a career in it. What if you don’t have much knowledge about Java and Linux? Experts say that you can still learn Hadoop course. While learning the framework, you can side by side learn Java and Linux for a few hours every day. Role of Java Though Java is not an exact prerequisite for Hadoop, knowing the basics is an added advantage. But, tools like Pig and Hive built on Hadoop have their own high-level languages to work with data. Writing code can be done in any language like Python, C, Ruby, Perl etc. These languages enable reading from the standard input and writing to the standard output with Hadoop streaming. In addition, the frameworks like Pig and Hive provide high level abstractions which do not require you to know Java. While Hive is programmed using HiveQL, Pig can be programmed in Pig Latin.  Both these are automatically converted to MapReduce programs in Java. Benefits of knowing Java Though you can use streaming to write the map and reduce the number of functions in the language you choose, there exists some advanced features that are only available in Java API at present. At times, you may have to check the Hadoop code in detail to find out why something is behaving like it is or to get more idea about the functionality of a particular module. This is again one situation when Java becomes useful. Many Hadoop projects come with roles like developer, tester, network administrator etc. some of which need Java knowledge while some don’t. If you are keen to dive into Hadoop administration then you can do so without any hesitation even if you don’t know Java. In course of time, you can improve your knowledge of Java so that you can be an expert in Hadoop. Role of Linux As you must be aware, Hadoop was initially built on Linux though it now runs fine in Windows. So, Linux is preferred for installing and managing Hadoop. Having a good foundation in Linux will help you grasp Hadoop better especially, the HDFS command line parameters. Role of C/C++ If you know to program in languages like C or C++, it would be easy for you to learn Java as they are not too different. So, learn some Java with your C/C++ knowledge and then move on to Hadoop training. You can also straight away start learning Hadoop with the C/C++ knowledge. There are many parts of the Hadoop system written in C and C++. If you can write map-reduce applications in C++ then Hadoop has pipes which is the library that permits C++ source code to be used for Reducer and Mapper code. Hadoop administrator certification Hadoop administration training prepares you for Cloudera’s CCAH certification and helps you build all the skills required for Big Data admin assignments. This specific course covers the Core Hadoop distributions which includes Apache Hadoop and Vendor specific distribution. You will learn about Cloudera manager and its capabilities along with the need for cluster management solutions. You will understand how to set up Hadoop cluster and its components like Pig, Hive, Sqoop etc. While taking the Hadoop admin certification, you will come to know about Hadoop’s Distributed File System and its computation/processing framework. You will also be taught to plan, secure, monitor and safeguard a cluster. Thus, the course will build a solid base in the advanced concepts of Big Data and the related technologies of Hadoop stack and the components in the Hadoop Ecosystem. After the completion of the course, you will have basic knowledge of Master Big Data with hadoop certification and its characteristics and the options used by companies to manage Big Data. You will understand the architecture of Hadoop framework and its distributed file system. Thus, learning Hadoop is one of the best options to go up in the admin career.

Who Can Do Hadoop course?

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Who Can Do Hadoop course?

Hadoop is a Java-based programming framework which is open source. This framework is useful in processing and storage of huge data sets in a distributed computing environment. It handles thousands of terabytes of data and runs applications on systems with thousands of hardware nodes. The data transfer rates among the nodes are very high and even allows the system to continue working in case a node fails.

Hadoop admin training lets you understand the Hadoop framework, HDFS and every related technology. It has four industry-based projects and is suited to data engineers, IT professionals, cloud administrators and system administrators. There will be different pros & cons in hadoop and more details about the course are given towards the end of the article.

Who is eligible to do Hadoop course?

Planning to learn Hadoop is a good decision if you are working in the Information Technology industry. There are no specific prerequisites to start learning the framework. But, it is recommended to know the basics of Java and Linux if you want to become a Hadoop expert and opt for a career in it.

What if you don’t have much knowledge about Java and Linux? Experts say that you can still learn Hadoop course. While learning the framework, you can side by side learn Java and Linux for a few hours every day.

Role of Java

Though Java is not an exact prerequisite for Hadoop, knowing the basics is an added advantage. But, tools like Pig and Hive built on Hadoop have their own high-level languages to work with data. Writing code can be done in any language like Python, C, Ruby, Perl etc. These languages enable reading from the standard input and writing to the standard output with Hadoop streaming.

In addition, the frameworks like Pig and Hive provide high level abstractions which do not require you to know Java. While Hive is programmed using HiveQL, Pig can be programmed in Pig Latin.  Both these are automatically converted to MapReduce programs in Java.

Benefits of knowing Java

Though you can use streaming to write the map and reduce the number of functions in the language you choose, there exists some advanced features that are only available in Java API at present. At times, you may have to check the Hadoop code in detail to find out why something is behaving like it is or to get more idea about the functionality of a particular module. This is again one situation when Java becomes useful.

Many Hadoop projects come with roles like developer, tester, network administrator etc. some of which need Java knowledge while some don’t. If you are keen to dive into Hadoop administration then you can do so without any hesitation even if you don’t know Java. In course of time, you can improve your knowledge of Java so that you can be an expert in Hadoop.

Role of Linux

As you must be aware, Hadoop was initially built on Linux though it now runs fine in Windows. So, Linux is preferred for installing and managing Hadoop. Having a good foundation in Linux will help you grasp Hadoop better especially, the HDFS command line parameters.

Role of C/C++

If you know to program in languages like C or C++, it would be easy for you to learn Java as they are not too different. So, learn some Java with your C/C++ knowledge and then move on to Hadoop training. You can also straight away start learning Hadoop with the C/C++ knowledge.

There are many parts of the Hadoop system written in C and C++. If you can write map-reduce applications in C++ then Hadoop has pipes which is the library that permits C++ source code to be used for Reducer and Mapper code.

Hadoop administrator certification

Hadoop administration training prepares you for Cloudera’s CCAH certification and helps you build all the skills required for Big Data admin assignments. This specific course covers the Core Hadoop distributions which includes Apache Hadoop and Vendor specific distribution. You will learn about Cloudera manager and its capabilities along with the need for cluster management solutions. You will understand how to set up Hadoop cluster and its components like Pig, Hive, Sqoop etc.

While taking the Hadoop admin certification, you will come to know about Hadoop’s Distributed File System and its computation/processing framework. You will also be taught to plan, secure, monitor and safeguard a cluster. Thus, the course will build a solid base in the advanced concepts of Big Data and the related technologies of Hadoop stack and the components in the Hadoop Ecosystem.

After the completion of the course, you will have basic knowledge of Master Big Data with hadoop certification and its characteristics and the options used by companies to manage Big Data. You will understand the architecture of Hadoop framework and its distributed file system. Thus, learning Hadoop is one of the best options to go up in the admin career.

Usha

Usha Sunil

Blog Author

Writing is Usha's hobby and passion. She has written widely on topics as diverse as training, finance, HR and marketing, and is now into technical writing and education. She keeps an interested eye on new trends in technology, and is currently on a mission to find out what makes the world go around.

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Ananthi 09 Jul 2017

Great and interesting article to read.. i Gathered more useful and new information from this article.thanks a lot for sharing this article to us..

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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.
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Overview of Deploying Machine Learning Models

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How Big Data Can Solve Enterprise Problems

Many professionals in the digital world have become familiar with the hype cycle. A new technology enters the tech world amid great expectations. Undoubtedly, dismay sets in and retrenchment stage starts, practice and process catch up to assumptions and the new value is untied. Currently, there is apparently no topic more hyped than big data and there is already no deficit of self-proclaimed pundits. Yet nearly 55% of big data projects fail and there is an increasing divide between enterprises that are benefiting from its use and those who are not. However, qualified data scientists, great integration across departments, and the ability to manage expectations all play a part in making big data work for your organization. It is often said that an organization’s future is dependent on the decisions it takes. Since most of the business decisions are backed by data available at hand. The accurate the information, the better they are for the business. Gone are the days when data was only used as an aid in better decision making. But now, with big data, it has actually become a part of all business decisions. For quite some time now, big data has been changing the way business operations are managed, how they collect data and turn it into useful and accurate information in real-time. Today, let’s take a look at solving real-life enterprise problems with big data. Predictive Analysis Let’s assume that you have a solid knowledge of the emerging trends and technologies in the market or when your infrastructure needs good maintenance. With huge amounts of data, you can easily predict trends and your future needs for the business. This sort of knowledge gives you an edge over your peers in this competitive world. Enhancing Market Research Regardless of the business vertical, market research is an essential part of business operations. With the ever-changing needs and aspirations of your customers, businesses need to find ways to get into the mind of customers with better and improved products and services. In such scenarios, having large volumes of data in hand will let you carry out detailed market research and thus enhancing your products and services. Streamlining Business Process For any enterprise, streamlining the business process is a crucial link to keeping the business sustainable and lucrative. Some effective modifications here and there can benefit you in the long run by cutting down the operational costs. Big data can be utilized to overhaul your whole business process right from raw material procurement to maintaining the supply chain. Data Access Centralization It is an inevitable fact that the decentralized data has its own advantages and one of the main restrictions arises from the fact that it can build data silos. Large enterprises with global presence frequently encounter such challenges. Centralizing conventional data often posed a challenge and blocked the complete enterprise from working as one team. But big data has entirely solved this problem, offering visibility of the data throughout the organization. How are you navigating the implications of all that data within your enterprise? Have you deployed big data in your enterprise and solved real-life enterprise problems? Then we would love to know your experiences. Do let us by commenting in the section below.
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How Big Data Can Solve Enterprise Problems

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Analysis Of Big Data Using Spark And Scala

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
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Analysis Of Big Data Using Spark And Scala

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