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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

Getting Started with Big Data Integration using HDFS and DMX-h

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Guest Blog Last Updated : 01 Aug, 2016
7 min read

Introduction

The data researchers no longer depend only on interviews, surveys, observational studies to collect data. Instead, they have switched to the faster ways of data collection which includes leveraging internet, cameras, smartphones, drones, bots and many more.

Later, the collected data is used by organization / governments to make business decisions. But, before that, they require a device or system which can store and secure such big data sets. One such system is Hadoop File Distribution System, commonly known as HDFS.

If you are new to HDFS architecture, let me provide a simple picture of what it does:

In simple terms, HDFS stores raw data is such a manner that the data is easily extracted at a later stage. Data is stored in clusters to enable parallel mode of extraction. After the data is stored in HDFS, we use query languages (such as SQL) to extract the data and use it for analysis / modeling purposes.

In this tutorial, we will look into basic concepts of Hadoop Distributed File System and the various components that make up HDFS. In the end, we’ll learn about a data integration tool DMX-h for practical understanding.

big data integration using HDFS architecture and DMExpress

 

What is Hadoop Distributed File System (HDFS)?

The ‘D’ in HDFS refers to breaking the files into various subsets and storing it in different clusters. This provides much needed pace and scalability for big data processing.

HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications which have large data sets.

It has a well structured architecture and incorporates MapReduce technique for processing and distributing large data sets. Let’s start with the architecture.

 

What are the components of HDFS Architecture?

There are 3 major components which supports HDFS architecture namely Client machines, Master nodes, and Slave nodes. Let’s understand each of them:

Master Node: It oversees the two key functional pieces that make up Hadoop: storing lots of data (HDFS), and running parallel computations on all that data (Map Reduce).

Slave Node: It makes up the vast majority of machines and do all the dirty work of storing the data and running the computations, hence the name ‘Slave’.

Client Machines: Client machines are those which have hadoop installed with default clusters settings and assist in loading data and steers the complete cycle of data processing.

Let’s dive deep into nodes. Nodes in HDFS are made up of a two components: Master Node and Multiple Worker Nodes. The master node consists of a Job Tracker, Task Tracker, NameNode and DataNode.

A name node manages the file system metadata and data node store the actual data. A data node stores data in the [Hadoop File System]. A functional filesystem has more than one DataNode, with data replicated across them. A slave or worker node acts as both a DataNode and TaskTracker, though it is possible to have data-only worker nodes and compute-only worker nodes.

Below are some important points which every big data / database analyst must know about HDFS:

  • In clusters, data is written only once. No random writing to files takes place.
  • Designed for large streaming data reads as opposed to random data reads.
  • On a Hadoop Cluster, data is stored on nodes in 128MB data blocks. This is a particular scenario, the data block size can vary depending on configuration and needs of the business.
  • Each data block is replicated on three nodes for fault tolerance.
  • It is anticipated that “inexpensive” Hadoop nodes will sometimes fail. Fault tolerance is an important concept with Hadoop.
  • Includes 5 Services, 3 Master (Name Node, Secondary Name Node, Job Tracker), 2 Slave (Data Nodes, Job Tracker).
  • Client requests Name Node to store files, Named Node will communicate back the data nodes available.
  • Client will then send the data files to the Data Nodes.
  • HDFS will also maintain redundant copies of the data, so that when a server crashes the other up servers can pick up.

 

What is MapReduce?

mapreduce framework movement across network

MapReduce is a processing technique and a program model for distributed computing based on java. The mapreduce algorithm contains two important tasks, namely Map and Reduce.

Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Reduce takes the output from a map as an input and combines those data tuples into a smaller set of tuples.

As the sequence of the name MapReduce implies, the reduce task is always performed after the map job.

 

Advantages of MapReduce

The major advantage of using MapReduce is that it is easy to scale data processing over multiple computing nodes.

MapReduce can be used to perform intensive operations such as change data capture. Change Data Capture is a processing intensive methodology used to make current data available to users. Because, it is so processing intensive, it often makes sense to perform the processing on Hadoop as opposed to Teradata or other platforms.

 

MapReduce Process Flow

Below is the complete process flow of MapReduce technique:

MapReduce – It steers the complete processing of data.

  • JobTracker takes care of processing the request.
  • JobTracker sends request to named node and apply the program on data.
  • Named Node provides metadata to Job Tracker.
  • JobTracker then assigns the task tracker to run the programs.
  • Then, task tracker runs the program on the nearest data node. This process is called as Map.
  • There are input splits (split the file into chunks), the number of mappers depends on input splits.
  • Task Tracker provides heartbeat back to the Job Tracker (this indicates task tracker is working fine).
  • Then, output is combined by Reducer. The number of output files depends on number of reducer.
  • When data node is gives heartbeat to the named node, if that data node has reducer, it provides that output information to named node. The client gets the output via named node which has metadata including the location of the output.

Until here, we discussed about theoretical aspects of data integration, let’s now practice it using DMX-h in order to develop even better understanding.

 

Using DMX-h for Data Integration

One of the key functions that is involved working with Hadoop/Big data is ETL (Extract, Transform, Load). In addition, we still moving data from HDFS/Map Reduce outputs to, let’s say, other traditional data warehouses, basically perform data integration. One of the tools that is available in the market today is called DMX-h from Syncsort .

It’s not an open source tool. Hence, for installation you need to create a one time account for installation here.

There are 2 major components of DMX-h. They are:

  1. DMX-h Task Editor : It deals with source files and target files.
  2. DMX-h Job Editor : It deals with adding tasks to jobs and running them eventually.

 

Task Editor Explained

The DMX-h Task Editor provides the feature to create tasks that deal with movement of data. If you are an experienced professional, the interface in certain ways might remind you of SQL Server Integration Services (SSIS).

The following demo shots explain how data can be copied from source to target.

1. Right Click on Source and Target to add the source and destination location of the files.

2. DMExpress Task Editor

3. DMExpress Task Editor1

4. DMExpress Task Editor2

 

2. Now move to target file. It simply means assigning a file to the target location.

5. DMExpress Task Editor-Target

DMExpress Task Editor-Assign Target

3. Once the source and target file locations have been assigned, the task is saved in the DMX-h Task Editor.

 

Job Editor Explained

Now, we move to the DMX-h Job editor where we can load all the tasks created in the DMX-h Task Editor. Then, we connect them according to the data transformation requirements.

4. In the screen shot below, we load the Sample_File_Copy Task created in the Task Editor to the Job editor.

6. DMExpress Job Editor

5. Now, we’ll run the job in the editor with the results of execution of the job (for privacy purpose, username is masked).

DMExpress Job Editor-Run Job_Edited

Once the job has been completed, we should verify if the source file has been successfully copied to the target location. Here I have demonstrated a simple example, this can be expanded to perform CDC operations and MapReduce Functionality.

6. The screenshot below shows how we can have different tasks (sort, split) in a job editor and also how we use the MapReduce component in the Job Editor.

8. DMExpress Job Editor-Map Reduce

 

End Notes

I hope this article provided you with the basic construct of HDFS and how tools like DMX-h can be used to perform different file operations on a Hadoop Cluster. In the end,  I would like to thank Debra Aaron (from bank of America) for providing crucial inputs to the article.

In this article, we discussed about the basics of HDFS & Mapreduce and its practical implementation using DMX-h tool. While writing this article, I was keen to understand the role of open source tools in Big Data. If anyone of you have any experience, I would love to interact in comments.

About Author132da2c

Ramdas Narayanan is  currently working as a Data Architect at Bank of America with focus on Business Intelligence and Data integration for Mortgage Technology. He has more than 22 years of experience in Information Technology,  ERP and Relational Database Systems.

Did you like reading this article ?  Do share your experience / suggestions in the comments section below.

Got expertise in Business Intelligence  / Machine Learning / Big Data / Data Science? Showcase your knowledge and help Analytics Vidhya community by posting your blog.

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Responses From Readers

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Ramdas
Ramdas

Thank you Manish for working with me and providing constructive feedback in order to get the article published. I would like to thank Manish and team at analytics vidhya for providing me with this opportunity and also providing encouragement for my desire of publishing articles. Thank you Ramdas

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Sanjay
Sanjay

good one . I was looking for this kind of tool .

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Ramdas
Ramdas

Thank you Sanjay

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