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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

How to Implement a Data Pipeline Using Amazon Web Services?

Sriram
Sriram Last Updated : 06 Feb, 2023
6 min read

Introduction

The demand for data to feed machine learning models, data science research, and time-sensitive insights is higher than ever thus, processing the data becomes complex. To make these processes efficient, data pipelines are necessary. Data engineers specialize in building and maintaining these data pipelines that underpin the analytics ecosystem. In this blog, we will discuss the end-to-end guide on implementing a data pipelines using Amazon Web Services.

Learning Objectives

  1. Understanding the importance of Cloud computing in this era of data.
  2. Identifying what data pipelines are and their need in solving different problems.
  3. Developing knowledge about Amazon Web Services and their applications in different industries.
  4. To Construct an overview for implementing the data pipelines using AWS.
  5. Practically implementing end-to-end data pipelines using AWS.

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

Table of Contents

  1. What is Cloud Computing and its benefits?
  2. What are Amazon Web services?
  3. What are Data pipelines and the need for them?
  4. What is AWS Data Pipeline?
  5. Processes involved in implementing the AWS data pipeline?
  6. Practical Implementation of AWS Data Pipeline.
    6.1 DynamoDB
    6.2 Amazon Simple Storage Service (S3)
    6.3 AWS Data Pipeline Service
    6.4 Parameters
    6.5 Schedule
    6.6 IAM Roles
    6.7 AWS Data Pipeline in Architect View
  7. Conclusion

What is Cloud Computing and its Benefits?

Cloud Computing facilitates the delivery of on-demand resources through web-based tools and applications. Cloud computing can reduce the expensive infrastructure, thus giving businesses the leverage to pay for only the resources used.

aws
Source: Freepik

Cloud computing allows the scalability of resources up and down based on business needs. Cloud Computing protects businesses from data loss and provides robust backup and recovery. Many cloud providers provide advanced security to help businesses to protect sensitive data and data breaches.

What are Amazon Web Services?

Amazon Web Services is a cloud computing platform that provides over 200 computing services that make up the cloud computing platform. Amazon Web Services offers different categories of services for storage, databases, analytics, deployment, and more.

The Major application of AWS includes the following:

  1. Storage and Backup:
    • Amazon Simple Storage Service
    • DynamoDB
    • Amazon RDS
  2. Big Data Management and Analytics:
    • Amazon EMR processes large amounts of data.
    • Amazon Kinesis to analyze the data.
    • AWS Glue to perform ETL ( Extract, Transform, Load ).
    • Amazon Athena to query data, Amazon quick sight to visualize data.
  3. Artificial Intelligence
  4. Internet of Things

What are Data Pipelines and the Need for Them?

Data Pipelines includes repetitive steps to automate data movement from its source to its final destination, processing the information along the way. These pipelines are operated in data warehousing, analytics, and machine learning.

Due to the progress in technologies, the amount of raw data being generated is massive thus, processing, storing, and migrating data becomes very complex; data pipelines are required to make these processes efficient so that businesses can analyze these data to derive business value and improve their business.

What is AWS Data Pipeline?

AWS Data Pipelines is a cloud-based service that facilitates users to process, transfer, and access data between different AWS services, DynamoDB, and EMR, at designated time intervals. Automating these processes through AWS Data Pipeline facilitates easy and swift deployment of changes.

aws
Source: Amazon

For example, We collect data from different data sources like DynamoDB and Amazon S3, then perform EMR analysis to get daily EMR results. AWS pipeline can vary depending on the specific needs and requirements of an application.

Processes Involved in Implementing the AWS Data Pipeline

  1. Identify and gather the source of data that will be used in the data pipeline.
  2. Define a Pipeline outlining the source and destination of the data, transformation, and the actions to be performed like data extraction and data movement.
  3. Create the Pipeline: Use the AWS Management Console, AWS CLI, or AWS SDKs to create the pipeline.
  4. Activate the Pipeline: use the AWS Data Pipeline Dashboard to keep track of its status, and fix any problems that may arise.
  5. Observe the Pipeline: Observe the pipeline check for errors and fix the issues, and track the progress of data processing.
  6. Optimize the Pipeline: Optimize the pipeline by testing and refining the processes and updating the pipeline as needed.

Practical Implementation of AWS Data Pipeline

DynamoDB

DynamoDB – DynamoDB is a NoSQL database service; create a table on it with a unique table name and a primary key. Create the table with the following configuration.

aws

Adding Data to visualize the exporting of this DynamoDB table into an S3 bucket using the AWS Data pipeline.

aws

Amazon Simple Storage Service (S3)

  1. Create an Amazon simple S3 with a unique bucket name and select the AWS Region where the DynamoDB is deployed.

  2. Create the Bucket.

 

aws
Data Pipeline

AWS Data Pipeline Service

  • Create a Data pipeline with a suitable name and Description, which is optional.
  • Choose the Source: We are exporting the DynamoDB table to a Simple storage service.
  • Enter the name of DynamoDB that has been created.

Parameters

  • Select the Simple storage service bucket folder that has been created.
  • DynamoDB read throughput ratio can be the default.
  • Select the Region for the dynamodb table.

Schedule

  • Select between on a schedule or on pipeline activation
  • Here we selected “ on pipeline activation ”.
Data Pipeline

IAM Roles

AWS data pipeline requires 2 IAM roles as the following:

  • The Pipeline role controls AWS Data pipeline access to AWS resources.
  • The EC2 instance Role controls the application running on EC2 instances that have access to AWS resources.
Data Pipeline

AWS Data Pipeline in Architect View

Data Pipeline

Activate the Pipeline

Data Pipeline

Two EC2 instances are deployed by the EMR cluster initiated by the pipeline.

The below image refers to the two EC2 instances

Data Pipeline

The below image refers to the EMR Cluster

After a few minutes, The exported data from the DynamoDB will be delivered to the S3 bucket we configured earlier. These data will further contribute to the processes.

Data Pipeline

 

Conclusion

In conclusion, AWS Data Pipeline is an efficient tool for transferring, processing, and altering data within AWS. Its automation capabilities and compatibility with multiple AWS services streamlines the data pipeline process, making it easy to deploy changes quickly. Whether you’re working with data warehousing, analytics, or machine learning, AWS Data Pipeline is a valuable tool that can help you manage your data more effectively. In short, AWS Data Pipeline is a must-have tool for those looking to optimize their data pipeline process.

Key Takeaways

  • Understanding the basics of cloud computing and Amazon Web Services and its applications in today’s world.
  • Learning about the importance of Data pipe in analyzing big data efficiently.
  • Practically implementing data pipelines using Amazon Web Services.

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

AWSAWS cloud searchblogathonCloud ComputingData Pipelinedata warehouseDynamoDB
Sriram
Sriram

I'm a machine learning enthusiast with a passion for unlocking the potential of AI to solve complex problems. Constantly seeking to improve my skills and knowledge, I enjoy exploring the latest advancements in this exciting and rapidly-evolving field.

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