Hadoop 2.8

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Hadoop is a software framework for storing data and running applications on clusters of commodity hardware. Hadoop solves big data problems and can be considered as a suite which encompasses a number of services (ingesting, storing, analyzing and maintaining) inside it. A Java-based framework, Hadoop is extremely popular for handling and analyzing large sets of data. It delivers massive storage for any kind of data, huge processing power and the ability to handle virtually limitless coexisting jobs or tasks.

Miri Infotech is launching a product which will configure and publish Hadoop eco-system which is embedded pre-configured tool with Ubuntu 16.04 and ready-to-launch AMI on Amazon EC2 that contains Hadoop, HDFS, Hbase, drill, mahout,pig,hive ,etc.

Hadoop saves the user from having to acquire additional hardware for a traditional database system to process data. It also reduces the effort and time required to load the data into another system as you can process it directly within Hadoop.

Importance of Hadoop

  • Capacity to store and process great amounts of any kind of data, quickly.
  • It’s a distributed computing model processes big data fast.
  • Application and Data processing are protected against hardware failure.
  • It is flexible, unlike traditional relational databases. With Hadoop, you don’t have to preprocess data before storing it.
  • You can easily develop your system to handle more data simply by adding nodes.

Our offering:

  • Hadoop 2.8.4: It is a framework that allows the creation of parallel processing applications on huge datasets, distributed across networked nodes.
  • Spark 2.3.0: Spark is a fast, in-memory data processing engine with elegant and expressive development APIs to enable data workers to efficiently execute streaming, machine learning or SQL workloads that require quick iterative access to datasets.
  • Scala 2.11.6: Scala is a programming language that expresses the programming patterns in a concise, elegant, and type-safe way; it can be easily integrated with Java. It supports functions, immutable data structures and gives preference to immutability over mutation.
  • Mahout 0.13.0: Mahout produces free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering, and classification.
  • Drill 1.13.0: The main purpose of Drill is large-scale data processing including structured and semi-structured data. It is a low latency distributed query engine that is used to scale to several thousands of nodes and query petabytes of data.
  • Hive: Hive is an open source data warehouse system for probing and analyzing large datasets stored in Hadoop files. Hive do three main functions: query, data summarization, and analysis.
  • Pig 0.17.0: Pig is an advanced language platform for querying and analyzing huge dataset, which is stored in HDFS. Pig as a component of the Hadoop Ecosystem uses PigLatin language.
  • Avro 1.8.0: Avro is an open source project that offers data serialization and data exchange services for Hadoop. It serializes the data into a compact binary format that can be desterilized by any application.
  • Thrift 0.11.0: Thrift enables you to define data types and service interfaces in a simple definition file. It syndicates a software stack with a code generation engine to build services that work efficiently and effortlessly across programming languages.
  • Hbase 0.98.8: HBase is a distributed database, designed to store structured data in tables that could have billions of row and millions of columns. HBase is distributed, scalable, and provides real-time access to read or write data in HDFS.
  • Zookeeper 3.4.12: Zookeeper is a consolidated service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. Zookeeper manages and synchronizes a large cluster of machines.
  • Flume 1.6.0:Flume efficiently accumulates, aggregate and moves a great amount of data from its source and sends it back to HDFS.

You can subscribe to an AWS Marketplace product and launch an instance from the product’s AMI using the Amazon EC2 launch wizard.

To launch an instance from the AWS Marketplace using the launch wizard

  • Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/
  • From the Amazon EC2 dashboard, choose Launch Instance. On the Choose an Amazon Machine Image (AMI) page, choose the AWS Marketplace category on the left. Find a suitable AMI by browsing the categories, or using the search functionality. Choose Select to choose your product.
  • A dialog displays an overview of the product you’ve selected. You can view the pricing information, as well as any other information that the vendor has provided. When you’re ready, choose Continue.
  • On the Choose an Instance Type page, select the hardware configuration and size of the instance to launch. When you’re done, choose Next: Configure Instance Details.
  • On the next pages of the wizard, you can configure your instance, add storage, and add tags. For more information about the different options you can configure, see Launching an Instance. Choose Next until you reach the Configure Security Group page.
  • The wizard creates a new security group according to the vendor’s specifications for the product. The security group may include rules that allow all IP addresses (0.0.0.0/0) access on SSH (port 22) on Linux or RDP (port 3389) on Windows. We recommend that you adjust these rules to allow only a specific address or range of addresses to access your instance over those ports
  • When you are ready, choose Review and Launch.
  • On the Review Instance Launch page, check the details of the AMI from which you’re about to launch the instance, as well as the other configuration details you set up in the wizard. When you’re ready, choose Launch to select or create a key pair, and launch your instance.
  • Depending on the product you’ve subscribed to, the instance may take a few minutes or more to launch. You are first subscribed to the product before your instance can launch. If there are any problems with your credit card details, you will be asked to update your account details. When the launch confirmation page displays.

Usage / Deployment Instruction

Step 1: Open Putty for SSH

Step 2: Open Putty and Type <instance public IP> at “Host Name” and Type “ubuntu” as user name Password auto taken from PPK file

Step 3: Use following Linux command to start Hadoop Cluster

Step 3.1: sudo su

$ vi /etc/hosts

Take the Private Ip address from your machine as per the below screenshot and then replace the second line of your command screen with that Private ip address

Step 3.2: $ ssh-keygen -t rsa -P ""

This command is used to generate the ssh key.

Step 3.3:  cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys

This command is used to move the generated ssh key to the desired location

Step 3.4: ssh localhost

Step 3.5: hdfs namenode –format

You have to write “yes” when it prompts you – Are you sure you want to continue?

Step 3.6: start-all.sh

Step 3.7: After the above command executes successfully, you should check the below urls in the browser –

http://<instance-public-ip>:8088

http://<instance-public-ip>:50070

http://<instance-public-ip>:50090

For spark:

Step 1: Enter the command: $spark-shell

Step 2: hit the browser with : http://<instance-public-ip>:4040

For scala:

Step 1: Enter the command: scala

For mahout:

Step 1: Enter the command: mahout

For drill:

Step 1: Enter the command: drill-embedded

Step 2: hit the browser with : http://<instance-public-ip>:8047

For Hive:

Step 1: Enter the command: hive

For pig:

Step 1: Enter the command: pig

For avro:

Step 1: Enter the command: avro --version

For thrift:

Step 1: Enter the command: thrift –version

For HBase:

Step 1: Enter the command: start-hbase.sh

Step 2: hbase shell

For Zookeeper:

Step 1: Enter the command: zxServer.sh start

Step 2: Enter the command: zkCli.sh

For Flume:

Step 1: Enter the command: flume-ng version

For Hcatalog:

Step 1: Enter the command: hcat

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    The Apache Hadoop software library allows for the distributed processing of large data sets across clusters of computers using a simple programming model. The software library is designed to scale from single servers to thousands of machines; each server using local computation and storage. Instead of relying on hardware to deliver high-availability, the library itself handles failures at the application layer. As a result, the impact of failures is minimized by delivering a highly-available service on top of a cluster of computers.

    Hadoop, as a scalable system for parallel data processing, is useful for analyzing large data sets. Examples are search algorithms, market risk analysis, data mining on online retail data, and analytics on user behavior data.

    Add the words “information security” (or “cybersecurity” if you like) before the term “data sets” in the definition above. Security and IT operations tools spit out an avalanche of data like logs, events, packets, flow data, asset data, configuration data, and assortment of other things on a daily basis. Security professionals need to be able to access and analyze this data in real-time in order to mitigate risk, detect incidents, and respond to breaches. These tasks have come to the point where they are “difficult to process using on-hand data management tools or traditional (security) data processing applications.”

    The Hadoop JDBC driver can be used to pull data out of Hadoop and then use the DataDirect JDBC Driver to bulk load the data into Oracle, DB2, SQL Server, Sybase, and other relational databases.

    Front-end use of AI technologies to enable Intelligent Assistants for customer care is certainly key, but there are many other applications. One that I think is particularly interesting is the application of AI to directly support — rather than replace — contact center agents. Technologies such as natural language understanding and speech recognition can be used live during a customer service interaction with a human agent to look up relevant information and make suggestions about how to respond. AI technologies also have an important role in analytics. They can be used to provide an overview of activities within a call center, in addition to providing valuable business insights from customer activity.

    There are many machine learning algorithms in use today, but the most popular ones are:

    • Decision Trees
    • Naive Bayes Classification
    • Ordinary Least Squares Regression
    • Logistic Regression
    • Support vector machines
    • Ensemble Methods
    • Clustering Algorithms
    • Principal Component Analysis
    • Singular Value Decomposition
    • Independent Component Analysis

    Highlights

    • icon

      Application and Data processing are protected against hardware failure.

    • icon

      It is flexible, unlike traditional relational databases. With Hadoop, you don’t have to preprocess data before storing it.

    • icon

      You can easily develop your system to handle more data simply by adding nodes.

    Application Installed

    • icon Hadoop 2.8
    • icon hadoop