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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

Amazon S3: Everything You Need to Know

Ansaba
Ansaba Last Updated : 07 Dec, 2022
5 min read

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

Source: https://www.stratospherix.com/

 

Introduction

Amazon Web Services (AWS) is a cloud computing platform offering a wide range of services coming under domains like networking, storage, computing, security, databases, machine learning, etc.

AWS has seven types of storage services which include Elastic Block Storage (EBS), Amazon FSx for Lustre, Elastic File System (EFS), Amazon S3 Glacier, Simple Storage Service (S3), Amazon FSx for Windows File Server, and AWS Storage Gateway

Amazon Simple Storage Service, commonly called Amazon S3, is the most popular and one of the first services introduced by AWS. It stores data of any type or size and supports all file formats. Amazon S3 helps in content distribution, performing analytics on data, backup, and recovery for cloud-based applications.

What is Amazon S3?

Amazon S3 is an efficient cloud storage service with high security, reliability, and scalability focusing on object-based storage.

S3 is a flexible, cost-effective data storage with REST and SOAP interfaces. It can be integrated with other AWS services like Amazon RDS, EC2, Cloud Front, etc. Amazon S3 has a web-based user interface that helps store and retrieve data from anywhere on the internet.

How Does Amazon S3 Work?

Amazon S3

Source: https://aws.amazon.com/s3/

S3 has the following components :

  • Buckets
  • Objects

Buckets:

It is the top-level element of S3 and can be thought of as an object container, where the object denotes basic storage unit. In other words, the bucket is a logical container storing data in Amazon S3 buckets. They can be addressed in the following ways:

  1.  Canonical form
    http://s3.amazonaws.com/bucketname
  2.  Virtual hosting form
    http://bucketname.com/
  3.  Subdomain form
    http://bucketname.s3.amazon.com

Buckets can be managed in the following ways:

  1.  AWS SDK (Software Development Kit)
  2.  Amazon S3 REST API

Features of Bucket:

  • It does not support nesting, meaning a bucket can’t contain another.
  • Users can choose the location of their choice for the creation of a bucket.
  • Users can create policies and permissions which restrict access to buckets.
  • Bucket creation is possible through PUT request to http://s3.amazonaws.com along with the bucket name.
  • Bucket deletion is possible through a DELETE request.
  • The bucket can be deleted only when it contains no objects, meaning it should be empty.

Objects:

It represents the basic data storage element in S3 buckets. The object has the following components:

  • Key: It is the unique identifier that identifies each object in a bucket. Key denotes the name of the object. It is the string that indicates a hierarchy of directories. Using the key, it is possible to retrieve objects in the bucket
  • Value: It is the actual content that needs to be stored. It is made up of a sequence of bytes.
  • Metadata: It is the data about data that are being stored. It denotes a set of name-value pairs that stores data about an object.
  • Version ID: It is the system-generated string that uniquely identifies a specific version of an object
  • Access control list: It controls access to objects or files stored in S3 by granting permissions.

Amazon S3 Storage Classes

S3 Storage Classes

Source: https://catalog.us-east-1.prod.workshops.aws/

The different S3 storage classes are:

  • S3 Standard
  • S3 Standard-IA
  • S3 Intelligent-Tiering
  • S3 One Zone-IA
  • S3 Glacier
  • S3 Glacier Deep Archive

 

S3 Standard:

It is the default and the most expensive storage, class. It supports cloud migration processes to other classes, website hosting, and big data analytics. This is the best option when frequent data access is necessary.

S3 Standard-IA (Infrequent access):

It is good for backups, long-term storage, and disaster recovery-based use cases. It has a lower price for data and it is used when data is rarely accessed.

S3 Intelligent-Tiering:

It delivers automatic cost savings by moving data to the most cost-effective access tier, without having an impact on performance. In other words, it moves data between the Infrequent Access Tier and Frequent Access Tier.

S3 One Zone-Infrequent Access (S3 One Zone-IA):

It is good for data that is used infrequently but requires rapid access. It is helpful in secondary backup storage.

S3 Glacier:

This service stores data as archives where data access is infrequent. It provides low-cost and long-life archive storage. Also, it uses server-side encryptions to encrypt all data.

S3 Glacier Deep Archive:

It is the lowest-cost storage class and provides long-term data retention where data access is infrequent. Also, it has a minimum storage period of 90 days. It has 99.9% availability over a given year.

Why Amazon S3?

  • Amazon S3 automatically creates multiple data replicas, so it is never lost.
  • High security involves encryption features and access management tools that prevent unauthorized access.
  • Able to store large files of a size limit of 5 TB.
  • Less cost because of the pay-as-you-go model. Users have to pay depending on the amount of storage and time they use on S3.
  • Easy to manage because the resources can be managed through GUI (Graphical User Interface), CLI (Command Line Interface), and API (Application Program Interface).
  • S3 keeps multiple copies of a file to track changes over time, thus supporting versioning.
  • Objects can be created on the location of the user’s choice.
  • Highly durable hence data loss is much lesser. S3 provides object durability of “99.99999999999%”

Amazon S3 Use Cases

Some of the use cases of amazon S3 include:

Static Website Hosting:

Amazon S3 helps in hosting static websites. Hence users can use their domain. Serverless Web Applications can be developed using S3 and by using generated URLs, users can access the application.

Backup & Recovery:

Amazon S3 helps create backups and archive critical data by supporting Cross Region Replication. Due to versioning, which stores multiple versions of each file, it is easy to recover the files.

Low-cost data archiving:

It is possible to move data archives to certain levels of AWS S3 services like Glacier storage classes, which is one of the cheap and durable archiving solutions for compliance purposes; thus, data can be retained for a longer time

Security and Compliance:

Amazon S3 provides multiple levels of security, including Data Access Security, Identity and Access Management (IAM) policies, Access Control Lists (ACLs), etc. It supports compliance features for HIPAA/HITECH, Data Protection Directive, FedRAMP, and others.

Conclusion

Amazon S3 is an efficient object-based storage service used with REST and SOAP interfaces. Organizations can use this service for storing data relating to any business, such as web or mobile applications, analytics, backup, etc. Some companies using S3 are Siemens, Invista, Bristol Meyers Squibb, etc.

Key Takeaways:

  • Amazon S3 (Simple Storage Service) is an online file storage service by AWS.
  • S3 is fully scalable, efficient, fast, and reliable.
  • Basic storage units of Amazon S3 are called objects organized into buckets.
  • Different S3 storage classes comprise S3 Standard, S3 Standard-IA, S3 Intelligent-Tiering, S3 One Zone-IA, S3 Glacier, and S3 Glacier Deep Archive.
  • The areas where S3 proved useful include static website hosting, backup & recovery, low-cost data archiving, security and compliance.

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

Ansaba
Ansaba

I'm a cloud enthusiast and have done graduation in computer science. My skills include C, C++, Php, Python, Cloud services(AWS, GCP), shell scripting, MySQL, etc. I have hands-on experience and have done several projects and certifications in areas of Python, Php, MySQL, Cloud (GCP, AWS), and C++

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