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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

A Dive into the Basics of Big Data Storage with HDFS

Hari Bhutanadhu
Hari Bhutanadhu Last Updated : 06 Feb, 2023
6 min read

Introduction

HDFS (Hadoop Distributed File System) is not a traditional database but a distributed file system designed to store and process big data. It is a core component of the Apache Hadoop ecosystem and allows for storing and processing large datasets across multiple commodity servers. It provides high-throughput access to data and is optimized for large file transfers, making it a suitable solution for big data storage and processing needs.

HDFS
Source: tech.ebayinc.com

HDFS provides several benefits for storing and processing big data:

  1. Scalable: It is highly scalable, allowing one to store and manage large amounts of data without worrying about running out of storage space. It gives reliability and fault tolerance by replicating data across multiple nodes, reducing the risk of data loss for node failure.
  2. Cost Effective: It is cost-effective compared to traditional storage and uses less expensive goods than specialized storage systems.
  3. Fast Data: It provides fast data access and processing speeds, which are crucial for big data analytics applications.

The processing of large datasets in parallel makes good big data processing needs. It is a flexible, scalable, and cost-effective solution for big data storage and processing requirements.

Learning Objectives:
1. Understand what HDFS (Hadoop Distributed File System) is and why it’s important for big data processing.
2. Familiarize yourself with its architecture and its components.
4. See how it is used in various industries, including big data analytics, media, and entertainment.
5. Learn about integrating HDFS with Apache Spark for big data processing.
6. Explore real-world use cases of HDFS.

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

Table of Content

  1. Understanding HDFS Performance Optimization
  2. What is the Difference Between HDFS & Other Decentralized File Systems?
  3. Integrating HDFS with Apache Spark for Big Data Processing
  4. HDFS Scalability and Handling Node Failures
  5. Use cases of HDFS in Real-World Scenarios
  6. Conclusion

Understanding HDFS Performance Optimization

HDFS performance optimization involves several steps to ensure efficient and reliable data storage and retrieval. Some of the key areas where performance includes:

  1. Cluster Sizing: Properly sizing is critical to ensure good performance. The size should be determined based on the expected data size, the number of concurrent users, and the data access patterns.
  2. Block Size: The block size used can impact performance. Larger block sizes allow for more efficient data retrieval but in higher overhead for data replication and management.
  3. Data Replication: It replicates data blocks for fault tolerance. The balance of data availability and performance.
  4. Disk I/O: The performance can be impacted by disk I/O performance. It is important to use fast disks and to ensure that disks are not overutilized.
  5. Namenode Memory: The Namenode is the master node that manages metadata about the file system. It is important to allocate enough memory to the Namenode to ensure efficient data management.

Monitoring Regular monitoring of HDFS performance metrics, block replication time, data access latency, and disk I/O ongoing performance optimization.

hadoop
Source: informationit27.medium.com

What is the Difference Between HDFS & Other Decentralized File Systems?

This section will discuss the HDFS and other various file systems and identify which one works for you.

Hadoop Distributed File System:

  1. Designed for large-scale data storage and processing, particularly well-suited for large data sets batch processing
  2. Master-slave architecture: one node as NameNode to manage metadata, other nodes as data nodes to store actual data blocks
  3. Supports data replication for data availability and reliability

GlusterFS:

  1. Designed to provide scalable network-attached storage
  2. Can scale to petabytes of data
  3. Client-server architecture with data stored on multiple servers in a distributed manner

Ceph:

  1. Designed to provide object storage and block storage capabilities
  2. Uses a distributed object store with no single point of failure

IPFS (InterPlanetary File System):

  1. Designed to address the problem of content-addressable data storage in a peer-to-peer network
  2. Uses a distributed hash table (DHT) to store data across nodes in a network

Selection depends on the specific use cases and requirements:

  1. HDFS for large data processing
  2. GlusterFS for scalable network-attached storage
  3. Ceph for distributed object store
  4. IPFS for content-addressable data storage in a peer-to-peer network.
HDFS
Source: www.databricks.com

Integrating HDFS with Apache Spark for Big Data Processing

Integrating HDFS with Apache Spark for big data processing involves the following steps:

  1. Start a Spark Context: A Spark context is the functionality that can be created using Scala’s SparkContext class or Java’s SparkConf class.
  2. Load the data into Spark: To load the data into Spark, you can use the SparkContext.textFile method in Scala or the SparkContext.textFile path to the data.

Here is an example in Scala that shows to load a text file from HDFS into Spark and perform a word count:

import org.apache. Spark.SparkContext
import org.apache. Spark.SparkContext._
val sc = new SparkContext("local", "word count")
val textFile = sc.textFile("hdfs://:/path/to/file.txt")
val counts = textFile.flatMap(line => line.split(" "))
                  .map(word => (word, 1))
                  .reduceByKey(_ + _)
counts.saveAsTextFile("hdfs://:/path/to/output")
sc.stop()

HDFS Scalability and Handling Node Failures

HDFS scalability refers to the ability to handle increasing amounts of data and users over time. The following are ways it can handle scalability:

  1. Horizontal Scaling: It can be scaled horizontally by adding more nodes to handle increasing amounts of data.
  2. Data Replication: It uses data replication to ensure availability even in node failures. By default, it replicates each data block three times, providing data redundancy and reducing the risk of data loss.
  3. Federation: Its federation allows multiple independent HDFS namespaces for multiple applications to share, allowing for further scalability.

Handling node failures in HDFS involves the following steps:

  1. Detection of Node Failure: HDFS uses Heartbeats and Block reports to detect node failures. The NameNode periodically receives heartbeats from the DataNodes, and if it does not receive a heartbeat from a DataNode for a long time, the DataNode has failed.
  2. Re-replication of Data: HDFS automatically re-replicates data from failed nodes to other nodes in data availability, even in node failures.
  3. Removal of Dead Nodes: The NameNode periodically checks for dead nodes, removes them from up resources, and ensures that they remain balanced.
  4. Automatic Failover: HDFS supports automatic failover of the NameNode, allowing continued functioning even in a NameNode failure.
HDFS
Source: bigdatapath.wordpress.com

Use cases of HDFS in Real-World Scenarios

Below are some real-world use cases of the HDFS System

  1. Big Data Analytics: It is used in big data analytics to store and process large amounts of data. For example, companies may use HDFS to store and process customer data, and shopping behavior, to gain insights into consumer preferences and make data-driven decisions.
  2. Media and Entertainment: It is used in the media and entertainment industry to store and process large amounts of multimedia content, audio, and images. For example, video streaming services may use HDFS to store and distribute video content to millions of users.
  3. Healthcare: It is used in the healthcare industry to store and process large amounts of patient data and lab results. For example, hospitals may use it to store and process patient data to support personalized medicine and improve.
  4. Financial Services: It is used in the financial services industry to store and process large amounts of financial data, such as stock prices, trade data, and customer information. For example, banks may use HDFS to store and process financial data to support real-time decision-making and risk management.
  5. Government and Public Sector: It is used in the government and public sector to store and process large amounts of data, such as census data, social media data, and satellite imagery. For example, government agencies may use HDFS to store and process data to support decision-making and improve public services.

Conclusion

In conclusion, HDFS continues to be a popular system for storing and processing big data. The Apache Hadoop ecosystem, of which HDFS is a part, continues to evolve and improve tools and technologies for data storage and processing. In the future, HDFS and the Apache Hadoop ecosystem will continue to play a critical role in the world of big and innovative ways to store and process large amounts of data to support decision-making and drive advancements in the Apache Hadoop ecosystem. HDFS will continue to be a key player in big data for years to come.

Major takeaways of this article:
1. Firstly, we have discussed what an HDFS database is and its main benefits.
2. Then, we discussed some tips and tricks for optimizing the performance of HDFS, like Cluster Sizing, Block Sizing, or Data Replication.
3. After that, we discussed how to handle node failures in HDFS and make it scalable. Also discussed some other decentralized file storage systems.
4. Finally, we discussed how to integrate HDFS in Apache for big data processing and then discussed real-world use cases.

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

Big databig data analyticsblogathonHadoopHadoop HDFShdfs architechture
Hari Bhutanadhu
Hari Bhutanadhu

My self Bhutanadhu Hari, 2023 Graduated from Indian Institute of Technology Jodhpur ( IITJ ) . I am interested in Web Development and Machine Learning and most passionate about exploring Artificial Intelligence.

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Workings of Hadoop Distributed File System (HDFS)
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