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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 Data Engineering in Practice?

A
Andre Last Updated : 01 Dec, 2021
6 min read
This article was published as a part of the Data Science Blogathon.
Data Engineering
Image Source: GitHub

Table of Contents

  1. What is Data Engineering?
  2. Components of Data Engineering
  3. Object Storage
  4. Object Storage MinIO
  5. Install Object Storage MinIO
  6. Data Lake with Buckets
  7. Demo Data Lake Management
  8. Conclusion
  9. References

What is Data Engineering?

Initially, we have the definition of Software Engineering, as the branch of engineering capable of specifying, developing, executing, and maintaining programs, systems, and applications. That solves corporate business problems, helping companies to increase their competitiveness.
Within Software Engineering we have several specializations, such as social networks, ERP, Project Management, Mobile Apps, Networking Management, and Data Science, among others. We can understand Data Engineering as a subset of Data Science, which receives data from Big Data and prepares them to be used in a Machine Learning Model.

Data Engineering 

 

Data Engineering | Software Engineering

Image Source: GitHub

Data Engineering Components

 

Data Engineering Components

Image Source:GitHub

Data Engineering is formed by a wide range of software, which is executed from a flow, created by the Data Scientist.

  • Data Discovery
  • Observability
  • Governance
  • MLOps
  • Workflow
  • Notebooks
  • Formats
  • Data Lifecycle
  • Virtualization
  • Compute
  • Object Storage
  • Meta stores
  • Orchestration
  • Analytics Engine
  • Ingest – Data
  • Ingest – Application

In this article, we will implement Data Engineering, applying an Object Storage component, with data storage functionalities after extracting an external Big Data, later after data manipulation and processing, the data is stored in this Object component. Storage is already in a structured form to be consumed by a Machine Learning model.

Components of Data Engineering

Data Engineering can be classified by several components, which can be executed in isolation, or integrated into a data flow pipeline. These components can be run on cloud infrastructure or on-premises servers.

For the identification of new data, we have the Data Discovery component, for the capture and processing of logs we have the Observability component, for data management we have the governance components.

Data Engineering and its' practices

Image Source: GitHub

We can understand MLOps as an extension of DevOps applied to Data Science applications, where we can visualize two flow layers, the first the Data layer and the second the Machine Learning layer.

In the Data Layer, the Data Scientist executes the deployment process, through a continuous and cyclic pipeline, which starts with the Data Collection, which can be understood by the Classic ETL Extraction process. Then, we have the Data Ingestion job, which loads the data and indexes the pipeline, the Data Analysis job curates and selects the data, Data Labeling adds a tag to the data, to facilitate the future classification process, the job Data Validation checks the usability of the data, to finally be prepared for the Machine Learning layer.

In the figure below, we can see the flow of the Machine Learning layer, which receives the data prepared from the previous layer and starts the machine learning training. In the validation of the machine learning model, the model with the highest performance is adopted for use in the dataset. In Machine Learning validation, the validation is done with test data, after this last verification the Machine Learning model is exported to a cloud infrastructure, such as Google Cloud Platform, Microsoft Azure Machine Learning, or AWS Machine Learning Sage Marker.

Data Engineering | Machine Learning Operations

Image Source: GitHub

 

As a result of MLOps, after executing the Data layer and Machine Learning layer flows, the Data Scientist offers the user, business specialist, a customizable dashboard, with the insights and predictions generated by the MLOps architecture.

Machine Learning Operations 2

Image Source: GitHub

Object Storage in Data Engineering

Object Storage is a fundamental component in Data Engineering, with unlimited data storage capacity, guaranteed to Big Data, flexible and highly scalable data storage.

Currently, we have AWS S3 as the main Object Storage available in the Cloud, having a large number of companies, which host data from various applications.

We have services with equivalent functionality in Oracle Cloud, Google Cloud Platform, Microsoft Azure, and IBM Cloud.

In this article, we will detail the open-source Minio software, which can be run on an on-premise server, or a virtual cloud server like AWS EC2.

 

Below we have the main features of the object storage software:

  • Directed to Big Data
  • Complete Data Lifecycle
  • Data Durability
  • Data Availability
  • Unlimited Scalability
  • High Transfer Fee
  • Integrated with Identity Management
  • Autocorrect
  • data protection
  • Server-Side Encryption
  • elastic sizing
  • Flexible Connectivity Options
  • Easy Integration
  • Fault Tolerance

Object Storage – MinIO for Data Engineering

Object Storage MinIO is an Open Source Software, a good alternative to AWS S3, it can be easily installed on a Linux server, and customized similarly to AWS S3.

We have a complete dashboard where we create Buckets, grant access authorization per user, create groups, define IAM policies, view logs, and monitor access. These features are available in the free version.

Object Storage | Minio

Image Source: Minio

Install Object Storage – MinIO

  • Installing and Configuring Data Lake with Minio Server
  • Inside the DataScience directory create the data lake directory.
  • If you are using Windows open the Powershell terminal and run the command:
docker run -d -p 9000:9000 -p 9001:9001 -v "$PWD/datalake:/data" minio/minio server /data --console-address ":9001" --name minio
http://localhost:9001/login
[username]: minioadmin
[password]: minioadmin

Data Lake in Data Engineering

We implement Data Lake, through the Object Storage MinIO installed, in the ingest data stream from data extraction from an external source, the data is stored in the first bucket named Curated Bucket, later we have the second Bucket named Processing Bucket, and ending we have the third Bucket called Landing Bucket, in which the Machine Learning Model will access the data sets, for training and testing the model.

Data Lake

Image Source: GitHub

Bucket Management

Data Engineering | Bucket Management

Image Source: GitHub

Below is the Data Lake repository on GitHub:

https://github.com/DataScience-2021/Analytics-Vidhya/tree/main/%23Data-Engineering/datalake

Curated Bucket #1

Curated Bucket #1

Image Source: GitHub

Below is the Curated Bucket repository on GitHub:

https://github.com/DataScience-2021/Analytics-Vidhya/tree/main/%23Data-Engineering/datalake/curated

Processing Bucket #2

Data Engineering | Bucket Processing

Image Source: GitHub

Below is the Processing Bucket repository on GitHub:

https://github.com/DataScience-2021/Analytics-Vidhya/tree/main/%23Data-Engineering/datalake/processing

Landing Bucket #3

Landing Bucket

https://github.com/DataScience-2021/Analytics-Vidhya/blob/main/%23Data-Engineering/DataEngineering/Slide22.PNG

Below is the Landing Bucket repository on GitHub:

https://github.com/DataScience-2021/Analytics-Vidhya/tree/main/%23Data-Engineering/datalake/landing

In Landing Bucket, we have the output of the Machine Learning model, after training, testing, and validation of the model.

Follow the output of the model:

https://github.com/DataScience-2021/Analytics-Vidhya/blob/main/%23Data-Engineering/datalake/landing/performance-evaluation/employee_performance_evaluation.json

{"emp_id":10001,"satisfaction_level":38.0,"last_evaluation":53.0}
{"emp_id":10002,"satisfaction_level":80.0,"last_evaluation":86.0}
{"emp_id":10003,"satisfaction_level":11.0,"last_evaluation":88.0}
{"emp_id":10004,"satisfaction_level":72.0,"last_evaluation":87.0}
{"emp_id":10005,"satisfaction_level":37.0,"last_evaluation":52.0}
{"emp_id":10006,"satisfaction_level":41.0,"last_evaluation":50.0}
{"emp_id":10007,"satisfaction_level":10.0,"last_evaluation":77.0}
{"emp_id":10008,"satisfaction_level":92.0,"last_evaluation":85.0}
{"emp_id":10009,"satisfaction_level":89.0,"last_evaluation":100.0}
{"emp_id":10010,"satisfaction_level":42.0,"last_evaluation":53.0}
{"emp_id":10011,"satisfaction_level":45.0,"last_evaluation":54.0}
{"emp_id":10012,"satisfaction_level":11.0,"last_evaluation":81.0}
{"emp_id":10013,"satisfaction_level":84.0,"last_evaluation":92.0}
{"emp_id":10014,"satisfaction_level":41.0,"last_evaluation":55.0}
{"emp_id":10015,"satisfaction_level":36.0,"last_evaluation":56.0}
{"emp_id":10016,"satisfaction_level":38.0,"last_evaluation":54.0}
{"emp_id":10017,"satisfaction_level":45.0,"last_evaluation":47.0}
{"emp_id":10018,"satisfaction_level":78.0,"last_evaluation":99.0}
{"emp_id":10019,"satisfaction_level":45.0,"last_evaluation":51.0}
{"emp_id":10020,"satisfaction_level":76.0,"last_evaluation":89.0}
{"emp_id":10021,"satisfaction_level":11.0,"last_evaluation":83.0}
{"emp_id":10022,"satisfaction_level":38.0,"last_evaluation":55.0}
{"emp_id":10023,"satisfaction_level":9.0,"last_evaluation":95.0}
{"emp_id":10024,"satisfaction_level":46.0,"last_evaluation":57.0}
{"emp_id":10025,"satisfaction_level":40.0,"last_evaluation":53.0}

Demo Data Lake Management

Demo Data Lake Management

A brief demonstration of our Data Lake created, using the MinIO Object Storage resources, with three Buckets, the last being Landing Bucket will be accessed by the Machine Learning model.

Conclusion

This article aimed to show Data Science professionals how to use Data Engineering components in an agile way, to generate Machine Learning models with good performance and accuracy.

We show several software components, which are part of Data Engineering, with their main functionalities. We detail the Object Storage technically, we implement a Data Lake, with three buckets, which is integrated into a Machine Learning model.

In this way, I believe that it is good, to use the Object Storage MinIO, in the creation of a Data Lake for companies that are starting the journey in Machine Learning.

References

New Home

https://blogs.oracle.com/lad-cloud-experts-pt/post/o-que-e-object-storage-como-eu-utilizo

https://min.io/

Author Reference:

  1. Github
  2. Twitter
  3. Medium

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

A
Andre

Software Engineer & Data Scientist with SoftSkills: #ESG #Vision2030 #research #innovation #iot #bigdata #analytics #blockchain #deeplearning #dataviz

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