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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

How Does Amazon SNS Work?

Chetan
Chetan Last Updated : 21 Aug, 2022
6 min read

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

Introduction

Amazon Simple Notification Service (SNS) is a managed service that delivers messages from publishers to subscribers (also known as producers and consumers). Publishers communicate asynchronously by sending messages on a topic that serves as a logical access point and communication route for subscribers. Clients can subscribe to an SNS topic and receive published messages via any supported endpoint, including Amazon Kinesis Data Firehose, Amazon SQS, AWS Lambda, HTTP, email, mobile push notifications, and mobile text messaging (SMS).

Amazon SNS
Source: -https://aws.amazon.com

 

What is Amazon SNS?

The Simple Notification Service (Amazon SNS) sends push notifications from the application to subscriber end users or other programs. It is a fully messaging service for communication between applications (A2A) and people (A2P). It allows you to create a topic as a logical access point and communication channel. Each subject has a unique name that specifies the SNS endpoint where publishers can send messages and subscribers can subscribe to notifications.
Source: -https://aws.amazon.com

 

Features of the Amazon Simple Notification Service

These are some of the Features of SNS

  • Auto-scaling: As the volume of messages grows, it scales accordingly.
  • Message Encryption: Protects your messages using encrypted themes from unwanted and unknown access. When messages are transmitted to subscriber endpoints, they are decrypted.
  • Message filtering: This allows the subscriber to change the filtering policy so that he only receives relevant notifications.
  • When a message is sent to a topic, it is replicated and sent to multiple endpoints, called message branching. Asynchronous event notification is provided by Fanout, allowing for parallel processing.
  • Mobile Notification: User-initiated in-app actions or cloud-based business logic can be triggered. Distributing mobile push notifications for iOS, Android, Fire OS, Windows, and Baidu-based devices are cheap.
  • SNS and Email Messaging: Amazon SNS can send text and email messages (SMTP).

How does AWS SNS Work?

As mentioned earlier, AWS SNS sends messages to its endpoints using the Publisher-Subscriber paradigm. You can use Amazon SNS to filter messages for a larger number of subscribers or endpoints through mobile communications, SMS, and email.
  • Publishers, also known as producers, create and send messages to AWS SNS.
  • On the other hand, subscribers receive messages published by the SNS using supported network protocols such as Amazon SQS, AWS Lambda, or HTTP/HTTPS.
  • Below are the exact stages of the workflow that AWS SNS implements:
  • The publisher first sends the required notifications to the subscribers.
  • SNS Topic – Used to filter out messages that need to be distributed to different subscribers. As an AWS developer, you must create an SNS topic that will serve as a subscriber access point. Subscribers can receive news alerts on a selected topic using a topic.
  • The message is delivered to the subscriber via subscriber queues or microservices. To receive a message, subscribers must submit valuable information such as a URL, email address, or phone number. Below are some of the most common forms of SNS messages:
  • Automatic user alerts triggered by predefined thresholds and sent to specific users via SMS or email are known as application and system alerts.
  • Push email or text messages are two ways to communicate with people or groups (for example, news article alerts sent to newspaper subscribers).
  • Mobile push notifications are messages sent directly to mobile applications (for example, notification of any application update available for installation).

How does Amazon SNS Message Filtering Work?

How Amazon SNS uses message filtering to deliver only a subset of SNS topic messages to subscriber endpoints. For example, consider a company website that sends user queries to many backend servers.
Amazon SNS

Source:-https://docs.aws.amazon.com

 

This server network consists of:
  • First, 1 server to handle requests from “Type 1” users.
  • Second, 1 server to handle “type 2” user requests.
  • 1 data analysis server handles both “Type 1” and “Type 2” user requests.
  • Following are the six stages of implementing message filtering with Amazon SNS:
  • Create an Amazon SNS topic
  • The first step is to set up an Amazon SNS topic to send messages whenever a user requests the site.
  • Use the “AWS Services” search box to find the Amazon SNS console.
  • Click “Get Started with an Overview” on the SNS Console landing page, then click “Topics” from the left-hand menu.
  • Then, in the dialog box, click “Create Topic” and enter the name of your topic (“Topic 1”) in the “Topic Name” field (shown below).
  • To create and add a new topic to the “Topic” detail page, click “Create Topic” in the dialog box above.

Create Amazon SQS Queues

  • The next step is creating Amazon SQS queues that subscribe to the SNS topic. Backend web servers query user requests from the corresponding SQS message queue. For this example:
  • Queue_1 processes requests from “type 1” users.
  • Queue_2 handles requests from “type 2” users.
  • Queue_3 handles both “Type 1” and “Type 2” user requests.
  • Sign in to the AWS SQS console using your username and password.
  • Then enter the Ques’ name as “Queue 1” in “Queue Name.” Click on “Quick-Create Queue” with “Standard Queue” selected.
Repeat step b to create two new queues named “Queue 2” and “Queue 3”. All three SQS queues are listed on the SQS console page once created.

Benefits of Amazon Simple Notification Service

The benefits of AWS SNS are as follows:

  • Instant delivery: Works on a push-based system. It is sent once a message is posted on a topic and forwarded to many subscribers.
  • It’s cheap because it’s built on a pay-as-you-go basis, which means we only pay when we use the resources and you don’t pay any upfront fees.
  • Multiple endpoints are supported, making it versatile. Different endpoints can receive messages via different transport protocols, including email, SMS, Lambda, Amazon SQS, HTTP, and more.
  • Ease of use: The AWS web management console offers the simplicity of a point-and-click interface, making it a very straightforward service.

Amazon SNS Pricing

Amazon SNS comes standard with a generous free tier and is reasonably priced. No subscription is required; you simply pay for the services you use on your chosen endpoint type. We will be sent 1 million free mobile push notifications every month. After that, each delivered message is charged $0.5 per million, depending on the recipient’s area.

What is Amazon SNS Security?

Let’s see the Security of AWS SNS

  • To protect messages from unwanted and unexplained access, SNS offers encrypted topics. Encryption takes place on the server.
  • Amazon SNS supports VPC endpoints through AWS Private Link. We can use VPC endpoints to send messages to SNS topics from the VPC without going over the public Internet.
  • Use access policies to limit which endpoints a topic allows, who has permission to publish the topic, and under what conditions.
  • You can enable AWS X-Ray for messages traveling through Amazon SNS, making it much easier to track and analyze messages traveling to downstream services.
  • Advantages of SNS
  • Instant delivery: Push-based delivery is the foundation of SNS. This is the most significant difference between SNS and SQS. When you post a message on a topic distributed to several subscribers, SNS is a push.
  • Flexible: Flexible SNS supports multiple types of endpoints. Different endpoints can receive messages via different transport protocols, including email, SMS, Lambda, Amazon SQS, HTTP, and more.
  • Inexpensive: The SNS service is low cost because it is built on a pay-as-you-go basis, meaning you only pay when you use the resources, and there are no upfront costs.
  • User-friendliness.
  • Ease of use: SNS is straightforward with a point-and-click web-based AWS management console interface.
  • Simple architecture: SNS is used to simplify the messaging architecture by offloading message filtering and routing complexity from subscribers and publishers.

Conclusion

Amazon SNS is intended to be used with other Amazon Web Services, such as Amazon SQS and EC2. For example, applications running on Amazon EC2 can publish event/information changes to Amazon SNS and immediately distribute them to all other end users. In addition, the subscriber can choose Amazon SQS as the delivery mechanism that sends notifications to different SQS queues in parallel, providing message persistence and formal assurance that messages are delivered. If you use Amazon SNS, you should enable it on your mobile devices to use its features.
  • Features of AWS SNS include:- Auto-Scaling, Message Filtering, Mobile Notification, SMS, and Email messaging.
  • It allows you to create a topic as a logical access point and communication channel. Each subject has a unique name that specifies the SNS endpoint where publishers can send messages and subscribers can subscribe to notifications.
  • To protect messages from unwanted and unexplained access, SNS offers encrypted topics. Encryption takes place on the server. Amazon SNS supports VPC endpoints through AWS PrivateLink. We can use VPC endpoints to send messages to SNS topics from the VPC without going over the public Internet.
  • The SNS service is low cost because it is built on a pay-as-you-go basis, meaning you only pay when you use the resources, and there are no upfront costs.

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Chetan
Chetan

Data Analyst who love to drive insights by visualizing the data and extracting the knowledge from it. Automating various tasks using python & builds Real time Dashboard's using tech like React and node.js. Capable of Creaking complex SQL queries to fetch the accurate data.

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