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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

A Guide to Build your Data Lake in AWS

Ruchi
Ruchi Last Updated : 25 Apr, 2021
6 min read
This article was published as a part of the Data Science Blogathon.

Introduction

Data Lake architecture for different use cases – Elegant and scalable solutions

Data Lake in AWS image
Photo by Ian Battaglia on Unsplash

Introduction:

Data Analytics is a blanket term in some sense which includes the job of a Data Engineer, Data Analyst, Data Scientist, and many more. In large-scale projects, many would agree with me that the job starts at databases which include creating data architectures for reading data from heterogeneous sources like your regular on-premise database, data from ERP systems, CRM systems, marketing tools, social media sites, messaging platforms, etc. Then comes decisions involving how and where to store this data – on-premise, cloud, or a hybrid model; how to manage this data – loading strategies; which ETL tool to use for data massaging, etc.

Therefore, in a real-world scenario, it becomes extremely important for Data Analytics/ Data science professionals to get their hands dirty with messy data and wear their Data Engineer’s hat also for a large part. More specifically, learn to handle and work on data on Cloud as it is surely the way forward.

This article attempts to explain the concept of a data lake and gives an overview of a few of the important and popular data lake architectures serving different use cases.

What is a Data Lake?

By definition, a data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale [1].

Although the term ‘Data Lake’ was coined by James Dixon, founder of Pentaho in 2010, it has attracted many users lately since companies have realized the importance of unstructured data along with regular relational data. The advantage of using a data lake is that all files in their native format can be stored in storage file formats. It creates a big advantage when dealing with Big Data, image files, text files, logs, web content can reside in a single data repository in raw format and can be converted to actionable insights as and when required.

As more and more players move into the Cloud Computing space, the competition is making data hosting, computing, and security efficient and scalable. Every cloud player is offering a host of services in areas of storage, compute, ETL, security, analytics, machine learning, visualization, etc. basically a complete package for a company to build an analytics pipeline.

 

Why is there a need for a data lake when your organization already has a data warehouse?

Most likely this question will come to your mind as you start with your cloud journey. A data warehouse will host your relational database and if you have an efficient DWH it certainly is not a good idea to scrap away everything you have build over the years. It is perfectly okay for both approaches to co-exist.

Your DWH will continue to store and process transactional data and data which is required by users who consume daily or monthly dashboards or reports for making data-driven decisions, basically your operational data. Data that is required for higher data use like data mining, running machine learning algorithms on unstructured data can go into your Data Lake. A point to note here is your structured data also can reside in Data Lake and cloud companies provide fully managed database services like AWS RDS (Relational Database Service) and Amazon Redshift.
As per the data lake definition above although it is a central repository, many layers or dedicated areas can be created inside a data lake for effective implementation. For example, a curated layer is a layer that will contain all your cleansed data. An analytics sandbox will be a dedicated area for data scientists to run algorithms and so on. This advantage will be only available in a data lake.

Why AWS?

Well, a very simple answer to this is that I have worked on AWS only till now and have found it extremely scalable and a complete solution. [This is my personal opinion and not a promotional article]

Also, going by the statistics online, AWS is the most preferred solution for companies.

Data Lake in AWS amazon
Source: Synergy Research Group (Image found Online)

 

Gartner report has also for the 10th consecutive year has named AWS as leading Market Platform.

Magic quadrant
Gartner Report 2020 [2]

 

Like most cloud platforms, AWS also offers a ‘Pay as you use’ approach, which means you only pay for what you use. So while creating your first data lake, even if you are not sure of the infra, you can start small and can scale as and when required.

Having understood the concept of a data lake and its importance vis a vis a data warehouse, we will look at two possible use cases and corresponding data architectures in AWS. While there can a be wide range of scenarios based on the requirements and budgets for the project, the idea behind selecting these two scenarios is to touch upon some important services offered by AWS.

Use Case 1 – Requirement of high-performance analytics pipeline with fully managed services

The above use case can be realized by using the S3 bucket as a landing area for heterogeneous data sources. AWS Glue is used as an ETL tool, and its related services like AWS CloudWatch for monitoring and security, and AWS SNS for notifications on the status of jobs are used. From AWS Glue, data is loaded to Amazon Redshift which provides a fully managed service to store highly structured and voluminous data.

AWS Glue also loads part data that is unstructured or semi-structured in the S3 bucket which forms a curated layer for querying data directly using AWS Athena giving users the power to query data in a SQL fashion. Data scientists can use S3 curated for mining data. To save storage space, S3 files can be stored in Parquet or JSON format and can be consumed directly.
BI analysts can directly connect to Redshift for generating reports and dashboards for operational use.

redshift
Image by Author

 

Use Case 2 – Requirement of low-cost analytics pipeline, have structured transactional data and flat files

For the second use case, a major difference is in the way data is stored after being loaded from AWS Glue. Instead of using AWS Redshift to store relational data, an Oracle instance is created on AWS Compute service called AWS EC2. Oracle on EC2 gives an advantage to companies to manage their databases and the cost is also low compared to AWS Redshift.

An added advantage being, that any other manipulations with data can be done in Oracle itself if the development team has strong Oracle skills. This highly cleansed data can be used by AWS QuickSight for analysis and data from S3 Curated can be used for creating ML algorithms.

Use Case 2
Image by Author

 

Conclusion:

This article covered concepts of a data lake and two frequently used data lake patterns in AWS. However, it’s just a tip of an iceberg. With every changing scenario, requirement, cost considerations, team skills, and new services being introduced by cloud platforms, there is lot to cover. For example, event-driven architectures or serverless architectures using AWS Lambda or AWS Fargate, the possibilities are large. Using EMR clusters with AWS Redshift can again be a useful combination for large and voluminous data.

Hope this article was helpful to readers starting their cloud journey.

References:

[1] https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/

[2] https://pages.awscloud.com/GLOBAL-multi-DL-gartner-mq-cips-2020-learn.html

 

About the Author:

I am an analytics professional with around 10 years of experience in domain areas of healthcare, FMCG and Banking.

Happy to connect!!

LinkedIn:  https://www.linkedin.com/in/ruchideshpande/

Profile on Medium: https://ruchideshpande.medium.com/

Ruchi
Ruchi

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Vignesh
Vignesh

Hi can you please provide the architecture for real-time streaming data and Batch Processing Data using AWS Stack (Pyspark, Glue, Redshift, S3, Boto3)

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