Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Introduction to Amazon Glacier & it’s Use Cases

T
Trupti Dekate Last Updated : 08 Aug, 2022
5 min read

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

Introduction

Amazon Glacier is a cloud storage platform developed and launched by AWS with longer loading times. Accordingly, the developer is supposed to use Amazon Glacier to move less available data to archive storage to save storage costs.

Amazon GlacierSource: https://www.subpng.com/png-mngaq
Developers can also move database backups to backup storage media to the cloud for long-term storage. Previously, companies paid a lot of money for data archiving. It is an important necessity within a company’s data management operations. Archiving solutions were previously available at a high cost, and companies had to determine the capacity requirement. This hampered the full functionality with several disadvantages such as unused capacity and unwanted financial expenses. Therefore, Amazon Glacier has taken this pain and brought a convenient and cheaper data backup and archiving solution. There is more to learn as you continue further in this article.

The Amazon Glacier Brought a Revolution

Amazon Glacier has changed the traditional way companies approach data archiving. With AWS Glacier, there are no upfront payment terms, and the cost per use is meagre. AWS works in the background to handle heavy operations to perform data storage needs. In addition, you gain the potential to increase or decrease the range of use. The AWS Management Console has direct options to easily create or set up an Amazon Glacier storage service. Once you do this, you will have access to upload a large amount of data of your choice.

Glacier archives contain sizes from 1 byte to 40 terabytes. In short, there is no limit to uploading data to an archive or creating multiple archives within AWS Glacier. There is a dedicated multipart recording function for high reliability and throughput of archives larger than 100 MB.AWS users can group archives into specific vaults and manage their accessibility through AWS Identity and Access Management (IAM). Each vault can be assigned a host, and notification policies can also be implemented with it. It is solely up to the AWS administrator to implement resource-based access policies for each archive vault. Access limits can therefore be set for hosts that are allowed access to selected archives. Along with archive vaults, there is a feature called “vault lock” under which compliance can be set for each. Once a lock is initiated, Amazon Glacier implements the configurations to meet compliance standards.

Amazon Glacier Pricing Policy

Amazon Glacier has a very fine-grained pricing policy where it charges for every gigabyte of monthly storage. Uploading data to Amazon Glacier is free, while the platform only charges for download requests. It is implemented to prevent customers from depending solely on Amazon Glacier for their general data storage needs and using it only as a repository for archives filled with infrequently used data.

Use Cases of Amazon Glacier

Storage has always been a basic necessity within IT infrastructure. All businesses today need to process and store a lot of data. Therefore, people need to store data preferentially for long-term needs. There are certain requirements embedded in a particular data set. This means that some data must be readily available in most cases, while some data is not required often. Therefore, storing such data in cold storage like Glacier is a smarter option.
use cases of Amazon GlacierSource: https://aws.amazon.com

 

By now, you have understood the basic usability of Amazon Glacier, but here are some of the use cases that will help you understand its use with more clarity:
1. Data archiving for regulatory purposes
Businesses are faced with the need to archive data for several regulations. It is mostly required in the healthcare industry as patient data is stored in large quantities for long periods. There can be no vulnerability in storing such sensitive data. However, the cost of keeping this much data for a decade or more can be extremely high. Therefore, people turned to AWS Glacier to offer an ideal storage service to meet the requirements of such companies or industries at a low-cost strategy.
2. Replacement of magnetic tapes
Tape libraries are still used in some applications, regardless of whether they are stored locally or off-site. Therefore, these aspects of storage are costly and require maintenance over time to ensure safety and reliability. Amazon Glacier is considered one of the ideal exchange options because it is integrated as a managed service. Plus, it charges you for the space or storage you use without any upfront payments. Storage Gateway from AWS can be used to store data archives in Amazon Glacier without disrupting existing workflows.
magnetic tapes | Amazon Glacier
Source: https://aws.amazon.com
3. Backup and restore for emergencies
Disaster recovery planning is quite important when working with or storing sensitive data. It is a key part of business and ensures protection during any emergency data crisis. It doesn’t matter if you run your business environment through the AWS cloud or any other hybrid cloud solution; Glacier can help you secure your data by backing it up without charging you.
Source: https://aws.amazon.com
In case you are facing a data crisis and are willing to recover the data stored in Glacier, several retrieval options will help you access it. RTO stands for Recovery Time Objective to decide the urgency of recovery. No matter what your RTO is, you can access your data instantly and in no time.
4. Archiving of digital media
Companies that deal with digital media such as news footage, major motion pictures, gaming projects, critical clippings, and more must preserve or archive that media long-term for future scenarios. Digital media are usually larger, such as gigabytes or terabytes. That is why their storage in cold storage is relatively effective so that they are secured in the long term. Without Amazon Glacier, you may have to pay a lot to store this digital media content online. Glacier is not only cost-effective but also equips you with easy search options whenever you need them.
5. Amazon Glacier Deep Archive Solutions
Amazon Glacier is working to improve products and introduce new features. That’s why AWS Glacier Deep Archive was introduced for users who require very long-term storage solutions at a low cost. With Deep Archive, data can be stored cost-effectively for more than ten years.

Conclusion

These are a few things that define the usability of AWS Glacier for businesses that deal with sensitive data being stored for long periods. Today, Amazon Glacier is recognized as a superior storage solution that is secure and resilient thanks to its services. So, ensure you understand how things work with AWS Glacier and its benefits before using it to streamline your business data storage needs. Log in to your AWS management console or create one to set up your Glacier account and upload tons of data without hassle.

  • It has numerous use cases with archiving options. Whether you’re part of an AWS environment, you can still use Amazon Glacier for your cold storage requirements. Glacier primarily uses AWS cloud infrastructure services and supports possible integration with third-party solutions.
  • The AWS Management Console has direct options to easily create or set up an Amazon Glacier storage service. Once you do this, you will have access to upload a large amount of data of your choice.
  • AWS works in the background to handle heavy operations to perform data storage needs. In addition, you gain the potential to increase or decrease the range of use.

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

Amazonamazon glacierblogathon
T
Trupti Dekate

I am an Accountant at Global private Analytics Services working with the Data Analysis Team for handling the budget of various Growing Companies. We provide service of analytics and made the work of new tech companies easy by helping them manage their total investment and giving suggestions.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
Amazon S3: Everything You Need to Know
Amazon S3: Everything You Need to Know
AWS Storage: Cost Optimization Principles
AWS Storage: Cost Optimization Principles

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details