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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Hadoop beyond traditional MapReduce – Simplified

Tavish Srivastava
Tavish Srivastava Last Updated : 26 Jul, 2020
5 min read

In previous articles on Hadoop, our focus have been on MapReduce routines. MapReduce are the basic functional unit of a Hadoop system. Following are the links of few articles published on Hadoop till date :

1. What is Hadoop? – Simplified!

2. Introduction to MapReduce

3. Tricking your elephant to do data manipulations (using MapReduce)

However with time we have progressed beyond MapReduce to handle big data with Hadoop. MapReduce, however exceptionally powerful becomes complex and time consuming when doing complete analysis on distributed network. Today, we have many more system which can work in conjunction with MapReduce or simply on HDFS to complete such complex functionalities. Our focus of this article will be to give an introduction to these systems or have an overview of Hadoop ecosystem beyond simple MapReduce.

Two types of MapReduce architectures

MapReduce Version 1: In most of the articles we were primarily referring to the first type of MapReduce architecture which is known as MapReduce Version 1. This architecture has been discussed in our previous article on Hadoop ( https://www.analyticsvidhya.com/blog/2014/05/hadoop-simplified/ ).

MapReduce Version 2 : Version 2 uses YARN cluster management system. Instead of Job Tracker and Task tracker now we have something known as Resource Manager, Application Master and Node Manager. This architecture is no longer dependent on converting every query to Map Reduce type. Following is a schematic of how YARN enables a few other tools to be operated on Hadoop. This diagram will be later shown with more details in the next section, where we will expand the section Others (data processing).

hadoop-1-to-2Extended Hadoop Ecosystem

Many tools which are mostly open source integrate into these two MapReduce architectures. These are not strictly core Hadoop systems but come under Hadoop Ecosystem. These tools help us to do any of the following :

  1. Data Analysis : Any analysis become extremely complicated if we directly use MapReduce. For instance, social network mining if done using MapReduce directly might end up becoming unnecessarily complicated. These analysis can be much simplified if we use these additional tools.
  2. Hadoop Workflow management : For corporates using Hadoop, it is very important to manage Hadoop workflow. Today we have many tools which can do this job for us.
  3. Data Transfer from one platform to other : Given that today we collect data from various sources, it has become very common to transfer data between platforms. We today have tools to do this job as well.

Following is a detailed schematic of the Hadoop ecosystem :

hadoopstack

Hadoop Ecosysted Tools – Brief introduction

APACHE PIG : PIG is an alternate way to writing detailed MapReduce functions. Just imagine this as an interpreter which will convert a simple programming language called PIG LATIN to MapReduce function. This interpreter operates on the client machine, where it does all the translation. Once this translation is completed, MapReduce functions are executed in the same way as directly written MapReduce functions work.

pig_open

APACHE HIVE : The functionality of HIVE is very similar to that of PIG. The only difference being the client side language. HIVE uses a similar query language to SQL known as HiveQL.  It also operates on the client processor and acts like an interpreter. However, it is very handy when it comes to niche analysis like sentiment analysis using Hadoop with a few built in functionality.

HiveCLOUDERA IMPALA : Impala is very similar to HIVE. However, for a few functionality it is extremely fast when compared to HIVE. But this is a project still in progress and hence is not used to the extent HIVE is used in the industry.

impala

APACHE SQOOP : Sqoop is a tool which comes in very handy when you want to shift data from RDBMS to HDFS or vice versa. This functionality of Sqoop is done using Map-only functions.Following is a general schematic how servers are connected together in a real life scenario :

sqoopsqoop1FLUME : Flume is in general used for high velocity data. Such as streaming data from social network or logs from server. The high velocity data can be processed on Hadoop server to create real time triggers. Following is a simple schematic of Flume network :

flume-logo

flume1 OOZIE :Oozie comes in handy to manage processing workflow. It is basically meant to handle, execute and cordinate between individual job in a Hadoop cluster. Such tool becomes very essential when Hadoop is installed in a corporate environment where getting a top view of the entire job being processed in parts becomes very essential.

oozie

HBASE : HBASE also uses HDFS and is capable of storing massive amounts of data. It can also handle high velocity data ranging to hundreds of thousands of inserts per second.

End Notes

Hopefully after reading this article, you might have got a good understanding of the Hadoop Ecosystem. As the data becomes bigger, necessity of Hadoop is bound to get stronger. In future articles we will focus on how does these components of Hadoop ecosystem actually work in detail.

Did you find the article useful? Share with us any practical application of Hadoop ecosystem you encountered in your work . Do let us know your thoughts about this article in the box below.

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Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Gaurav
Gaurav

I can not view the images in this article. Could you please check if the images have been removed?

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Kunal Jain
Kunal Jain

This should now be fixed. Do let me know, if you are still facing this challenge. Kunal

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