• PHP
  • Linux
  • Spark Mllib
  • Hadoop 2.7
  • Scala
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An AWS product Spark Mllib Hadoop Scala powered by Miri Infotech. MLlib is Spark's machine learning library, focusing on learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.

We are launching a product which will configure and publish Spark MLlib, an open source software solution which is embedded pre-configured tool with Ubuntu OS and ready-to-launch AMI on Amazon EC2 that contains Spark MBlib, Hadoop 2.7, Scala, Linux, PHP (LAMP).

MLlib fits into Spark's APIs and interoperates with Scala. You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it easy to plug into Hadoop workflows.

Why MLlib? It is built on Apache Spark, which is a fast and general engine for large scale processing. Supposedly, running times or up to 100x faster than Hadoop MapReduce, or 10x faster on disk. Supports writing applications in Java, Scala, or Python.

MLlib contains many algorithms and utilities
  • Classification: logistic regression, naive Bayes
  • Regression: generalized linear regression, survival regression
  • Decision trees, random forests, and gradient-boosted trees
  • Recommendation: alternating least squares (ALS)
  • Clustering: K-means, Gaussian mixtures (GMMs)
  • Topic modeling: latent Dirichlet allocation (LDA)
  • Frequent itemsets, association rules, and sequential pattern mining
  • MLlib will still support the RDD-based API in spark.mllib with bug fixes.
  • MLlib will not add new features to the RDD-based API.
  • In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.
  • After reaching feature parity (roughly estimated for Spark 2.2), the RDD-based API will be deprecated.
  • The RDD-based API is expected to be removed in Spark 3.0.
  • DataFrames provide a more user-friendly API than RDDs. The many benefits of DataFrames include Spark Datasources, SQL/DataFrame queries, Tungsten and Catalyst optimizations, and uniform APIs across languages.
  • The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.
  • DataFrames facilitate practical ML Pipelines, particularly feature transformations. See the Pipelines guide for details.
  • Data types
  • Classification and regression
  • Collaborative filtering
  • Clustering
  • Dimensionality reduction
  • Feature extraction and transformation


Usage / Deployment Instruction

Step1 : Open Putty for SSH

Step2 : Open Putty and Type <instanceID>> at “Host Name”

Step3 : Open Conncetion->SSH->Auth tab from Left Side Area

Step 4 : Click on browse button and select ppk file for Instance and then click on Open

Step 5 : Type "ubuntu" as user name Password auto taken from PPK file

Step 6 : Use following Linux command to start Hadoop

Step 6.1 : sudo 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 6.2 : ssh-keygen -t rsa -P ""

This command is used to generate the ssh key.

Step 6.3 : cat $HOME/.ssh/ >> $HOME/.ssh/authorized_keys

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

Step 6.4 : ssh localhost

Step 6.5 : hdfs namenode –format

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

Step 6.6 :

Step 6.7 : After the above command executes successfully, you should check the below urls in the browser -




Step 7 : Use following Linux command to start Scala and Spark

Step 7.1 : cd spark-2.1.0/

Step 7.2 : ./bin/spark-shell

Step 7.3 : You can check the spark by going on to the following url in your browser –


Step 7.4 : Now you can execute your scala programs as below –


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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
  • From the Amazon EC2 dashboard, choose Launch Instance.
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  • 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 ( 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.
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  • 1What is Hadoop?

    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.

  • 2Where does Hadoop find applicability in business?

    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.

  • 3What is big data security analytics?

    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.”

  • 4Is there an easy way to migrate data from Hadoop into a relational database?

    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.

  • 5Are Intelligent Assistants the only application of AI for customer care, or are there other ways that AI technologies can impact the contact center?

    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.

  • 6What are the popular machine learning algorithms in use today?

    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




  • Data types, Classification, regression and Collaborative filtering
  • It provides Clustering, Dimensionality reduction, Feature extraction and transformation
  • It provides logistic regression, naive Bayes, Decision trees, random forests, and gradient-boosted trees

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