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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

Workings of Hadoop Distributed File System (HDFS)

anushkakhatri
anushkakhatri Last Updated : 06 May, 2022
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

This article will discuss the Hadoop Distributed File System, its features, components, functions, and benefits. This article also describes the working and real-time applications. Hadoop is a powerful platform for supporting an enormous variety of data applications. Both structured and complex data can be processed using this interface, facilitating heterogeneous data consolidation. With HDFS, large data sets are handled by a distributed file system using commodity hardware.

What is HDFS?

Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. To implement a distributed file system that provides high-performance access to data across highly scalable Hadoop clusters, HDFS uses the NameNode and DataNode architecture. Apache Hadoop is an open-source framework for managing data processing and storage for big data applications. HDFS is a crucial part of the Hadoop ecosystem. It can manage big data pools and support big data analytics applications.

Components of HDFS

HDFS has two components, which are as follows:

1) Namenode

2) Datanode

Components of HDFS

 

Namenode

The NameNode is the master node of HDFS and stores the metadata and slave configuration. In HDFS, there is one active NameNode and one or more standby NameNodes. The Active NameNode serves all client requests, and the standby NameNode handles high availability configuration.

Functions of NameNode:

  • It manages the File system namespace and is the single Hadoop cluster failure point.
  • It keeps track of all blocks in HDFS and where each block is located.
  • It manages the client access requests for the actual data files.
  • The metadata about the actual data is also stored here, like File information, Block information, permissions, etc.

 

DataNode

DataNode is a worker node of HDFS, which can be n in number. This node is responsible for serving read and write requests to clients. Datanodes store actual data in HDFS, so they typically have a lot of hard disk space.

Functions of DataNode:

  • It stores actual data in HDFS.
  • As instructed by the NameNode, the DataNodes are responsible for storing and deleting blocks and replicating these blocks.
  • This node handles the client’s read and writes requests.
  • DataNodes are synchronized to communicate and ensure that data is balanced across the cluster, moving data for high replication, and copying data as needed.

 

Working of HDFS

HDFS is based on master-slave architecture. It can consist of hundreds or thousands of servers. Applications such as SQoop, NIFI, and Kafka Connect write data to HDFS. The client communicates with the NameNode for metadata, and the NameNode replies with the location of blocks, the number of replicas, and other attributes.

NameNode sends the High Availability Configuration information to Zookeeper, and it replicates the information to multiple Zookeepers. Zookeepers serve as an election commissioner by selecting one StandBy NameNode when an Active NameNode is down, and there are multiple StandBy NameNodes.

Using NameNode information, the client contacts the DataNode directly. Based on the information received from the NameNode, the client will start writing data directly to data nodes and reading data from the DataNodes in parallel.

Working of HDFS

 

Benefits of HDFS

The benefits of the Hadoop Distributed File System are as follows:

1) The Hadoop Distributed File System is designed for big data, not only for storing big data but also for facilitating the processing of big data.

2) HDFS is cost-effective because it can be run on cheap hardware and does not require a powerful machine.

3) HDFS has high fault tolerance since if a machine within a cluster fails, a replica of the data may be available from a different node through replication.

4) Hadoop is famous for its rack awareness to avoid data loss, which results in increased latency.

5) HDFS is scalable, and it includes vertical and horizontal scalability mechanisms so you can adjust the resources according to the size of your file system.

6) Streaming reads are made possible through HDFS.

HDFS Data Replication

Data replication is crucial because it ensures data remains available even if one or more nodes fail. Data is divided into blocks in a cluster and replicated across numerous nodes. In this case, if one node goes down, the user can still access the data on other machines. HDFS maintains its replication process periodically.

Applications of HDFS

The real-time applications range from search engine requirements, ad placement, large-scale image conversion, log processing, machine learning, and data mining. Also, several industries use this to manage pools of big data, a few of which are :

Electric companies:

The health of smart grids is monitored through the use of phasor measurement units (PMUs) installed throughout their transmission networks.  Selected transmission stations use these high-speed sensors to measure voltage and current amplitudes. These companies analyze PMU data and adjust the grid to detect segment network faults. With PMU networks logging thousands of records per second, companies take advantage of HDFS.

Marketing:

Targeted marketing campaigns require marketers to have a deep understanding of their target audiences. Marketers can gather this information from several sources, including CRM systems, direct mail, point-of-sale systems, Facebook, and Twitter. An HDFS cluster is the most cost-effective way to store this unstructured data before it is analyzed since it is unstructured.

Oil and gas companies:

The oil and gas industries deal with various data formats and massive data sets, including videos, 3D earth models, and machine sensor data. It is made possible by an HDFS to perform big data analytics on such data.

Conclusion

This article discussed Hadoop Distributed File System, its components, and their functions. In addition, we discussed the architecture and workings of HDFS, its benefits, and real-world applications.  Due to Hadoop’s open-source features, its commodity servers are more affordable to use with improved performance. Using Map/Reduce, users can manipulate the methods of analyzing their data. Let’s summarize the best key points we learned and should remember:

  • HDFS is fast, affordable, and scalable.
  • Hadoop and HDFS are excellent tools for handling big data, especially when you plan to grow at a large scale.
  • The NameNode failure is critical to the file system, but the BackupNode or a coordination service such as Zookeeper can maintain data availability during each service cycle.
  • Data Replication and Rack Awareness are essential features of HDFS, which help improve networking performance and prevent data loss.

 If you have any questions or comments, please post them in the comments below or connect with me on LinkedIn. 

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

Apache HadoopblogathonData EngineeringHadoop HDFS
anushkakhatri
anushkakhatri

Passionate about solving business problems with data-driven solutions. Skilled in the field of Data Science and Big Data Engineering.
I have worked on various projects, including developing predictive models, analyzing complex data sets, and designing and implementing data architectures and pipelines.
I enjoy exploring new data science and data engineering techniques and keeping up with the latest industry trends.

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