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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 Appflow and it’s Benefits

Chetan
Chetan Last Updated : 02 Sep, 2022
4 min read

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

Introduction

The cloud environment is constantly evolving, with AWS and Salesforce leading the way in innovation. It was just last year at Dreamforce that Salesforce and AWS announced a strong global partnership in the cloud market. At that time, they shared new innovative solutions, including the integration of Amazon Connect as part of Salesforce Cloud Voice, which opened up a whole new opportunity for organizations to provide superior customer service. At AllCloud, our leading AWS and Salesforce experts have been working on synergies between AWS and Salesforce to solve customers’ business challenges for years.

Amazon Appflow
Source: https://aws.amazon.com
And now Amazon recently released AppFlow, a fully managed integration service that allows you to securely exchange data between Software as a Service (Salesforce) and AWS. This new solution allows users an easy and cost-effective way to transfer data. It’s an excellent tool for Salesforce users who want to jump into AWS and see immediate benefits.

What is Amazon AppFlow?

Announced in April, Amazon AppFlow is a fully managed integration service that lets you securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow and AWS services like Amazon S3 and Amazon. Redshift is just a few clicks away.
With AppFlow, you can run data flows at almost any scale and frequency you choose—scheduled, in response to a business event, or on demand. You can configure data transformation functions such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, with no additional steps. AppFlow automatically encrypts data in motion. It allows users to restrict the flow of data over the public Internet for SaaS applications that are integrated with AWS PrivateLink, reducing exposure to security threats.
Amazon AppFlow allows you to do the following:
  • Get started fast — Create a data flow to transfer data between source and destination in minutes without writing any code.
  • Connect your data – Aggregate data from multiple sources to train your analytics tools more efficiently and save money.
  • Track your data — With Amazon AppFlow’s flow management tools, you can track what data has moved where and when.
  • Keep your data safe – security is a top priority. We encrypt your data at rest and during transmission.
  • Transfer data privately – Amazon AppFlow integrates with AWS PrivateLink to provide private data transfer over the AWS infrastructure instead of public data transfer over the Internet.

 

What are the benefits of AppFlow?

AppFlow frees you from investing significant time and highly skilled developers in creating and maintaining custom API connectors for AWS services to exchange data with SaaS applications. SaaS application administrators and business analysts can quickly implement most of the integrations they need without IT having to wait months for integration projects to be completed. Specifically, the benefits include:
  • Speed ​​and agility: Amazon AppFlow lets you integrate apps in minutes—no more days or weeks waiting to code custom connectors. Features like data paging, error logging, and network retries are included by default, so there’s no coding or administration involved. With Amazon AppFlow, the quality of the data flow is integrated, and you can enrich the data flow with masking, mapping, merging, filtering, and validation as part of the flow itself.
  • Security and Privacy:  You can encrypt data using AWS-managed keys or bring your keys. It also allows users to throttle the flow of data over the public Internet using Amazon VPC endpoints enabled by AWS PrivateLink. This minimizes the threat of internet attacks and the risk of leaking sensitive data.
  • Scalability: AppFlow scales easily without planning or provisioning, so you can move large volumes of data without breaking it into multiple batches. With Amazon AppFlow, you can quickly transfer millions of Salesforce records or Zendesk tickets—all while running a single flow.
  • Reliability: AppFlow uses a highly available architecture with redundant, isolated resources to prevent any single failure while running within AWS’ resilient infrastructure.

Unique Pricing Structure

The pricing model makes AppFlow even more attractive. Although there are many similar tools in the market, none of them can provide the cost value of AppFlow. There are no upfront fees to use AppFlow, and customers only pay for the number of flows they run and the amount of data processed. You pay for every successful stream. Initiating a flow is a call to the source application to transfer data to the destination. Flow runs to check for new data count towards your flow running costs, even if there is no new data on the source system to transfer.
Amazon AppFlow’s monthly processed data by volume is billed per GB and aggregates all flows that exist in an AWS account. Data processing refers to schema mapping, filtering, and field validation to name a few. With Amazon AppFlow, you are not responsible for AWS data transfer fees and only pay the data processing costs for the flows.

How does Amazon AppFlow affect salesforce users?

AppFlow enables third-party applications on AWS to update Salesforce and vice versa, leveraging Amazon AppFlow’s bidirectional capabilities and changing data capture capabilities. AppFlow allows you to easily create automated and rich data that spans multiple applications. AppFlow offers many additional features that the default Salesforce data loader cannot effectively provide. Salesforce users can instantly extract data object by object and store it in Amazon S3. This gives Salesforce users the ability to move data without quickly purchasing an expensive integration tool.

Conclusion

AppFlow lets you connect and map Salesforce objects and fields in minutes. This allows you to run and schedule through the AWS AppFlow UI without having to worry about additional licensing. For example, if you collect data about how your customers interact with apps and websites, and you need to transfer it to S3. This is something that Salesforce cannot do because a Salesforce database cannot directly connect to a database running SQL. AppFlow allows you to efficiently combine all these data points into an S3 environment. And if you’re already using S3 for your data lake, the benefits are even better.
  • Amazon AppFlow is a fully managed integration service that lets you securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow and AWS services like Amazon S3 and Amazon. Redshift is just a few clicks away.
  • Amazon AppFlow’s monthly processed data by volume is billed per GB and aggregates all flows that exist in an AWS account. Data processing refers to schema mapping, filtering, and field validation to name a few.
  • AppFlow frees you from investing significant time and highly skilled developers in creating and maintaining custom API connectors for AWS services to exchange data with SaaS applications.

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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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