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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

Data Engineering – A Journal with Pragmatic Blueprint

P
Pavithra Last Updated : 23 Jun, 2022
5 min read

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

Introduction to Data Engineering

In recent days the consignment of data produced from innumerable sources is drastically increasing day-to-day. So, processing and storing of these data has also become highly strenuous. Emerging technologies such as Data Engineering, Artificial Intelligence and Machine learning algorithms help us to handle these data intelligently and make them useful for business needs. This article concentrates much on the steps in Data Engineering and how it is performed with Real Life examples. The process of data engineering is to make the data available and accessible to the data-driven jobs and this can be achieved via ETL (extract, transform, and load) pipelines.

Data Engineering
OWN Image: Process of Data Engineering

Data Extraction

so, the first step in data engineering is to extract the data from various sources like IoT devices or some batches of videos uploaded periodically or medical reports etc. The data could be of any type but the aim here is to take the data reliably and process it.

Data Transformation

After extraction, we do preprocess the data to convert it to the required format. Because the data from various sources will not always fit for querying and analysis. So, we’re modifying it to make it in a more suitable format. In the Transform stage of the ETL pipeline, we do the following steps usually,

1) Normalization and Modeling

2) Data cleaning

Normalizing the data helps to make it more accessible to the users. Also, this can be achieved in 3 major steps,

· Removing duplicate data.

· Handling and fixing the conflicts in the data.

· Making sure the data is in the specified data model.

Some more steps can also be added here but these are the major steps involved in data normalization.

Then, Data cleaning is associated with data normalization but the main difference here is that data cleaning will include some steps to make the data more uniform and complete while the data normalization focuses only on making the heterogeneous data fit into some data model.

So, below are the some of the major steps involved in data cleaning,

· Typecasting the data if required.

· Keeping all the date and time fields in the same format.

· Elimination of corrupt or unusable data.

· Identifying the missing fields and fill the appropriate data if possible.

Data Storage

After doing the transform, the next step of the ETL pipeline is load. Here we do store the transformed data in a particular location and in a specified format for the better accessibility of the users. These modified data will be stored in Data Lakes and Data Warehouse usually.

Where the data will be stored in data lakes immediately after extraction as raw data. It gives high agility to the data processing as it is not limited to the data warehouse’s fixed configuration. Also, unprocessed data will be used by the data scientists for doing an exploratory data analysis.

On the other hand, a Data warehouse is a central repository where the data is stored in the query-able form. A data warehouse can be able to store both structured and unstructured data which include various file formats like PDFs, images, audio, and video files.

Batch Processing Pipeline

Nowadays data Engineers are mainly using Lambda Architecture for processing real-time data.

Lambda Architecture(LA) helps to support Unified pipelines and real-time data processing. LA is designed based on 3 Layer (Batch Layer, Serving Layer and Speed Layer) that helps to process the real-time data based on batch processing or stream processing method with a hybrid approach.

Batch Layer:

The main work of this layer is to Manage the master Dataset and Pre-compute the batch views. So, whatever data comes will be fed into the batch layer as well as the Speed Layer which will help the real-time processing.

Serving Layer:

This layer takes the near real-time views from the speed layer and the batch views which come from the batch layer and indexes them. These Indexes are queryable formats of the latest batch views.

Speed Layer:

This layer creates a real-time view of recent data to provide a complete view of the data for the user to process.

Stream Data Pipeline

Since the data engineering field has grown significantly, there is a huge demand for engineers to work in the traditional batch processing method. Nowadays many organizations are following multiple batch processing while few organizations started working on stream processing data pipelines. This is mainly because of the complexity of issues involved in the stream processing method to work as expected. These complexities have been overcome with the introduction of powerful and easy-to-use frameworks such as Apache Flink, Apache Spark, etc.

Example:

Let us consider a real-time scenario that helps us get a better understanding of the field of data engineering. For example, we will consider a paid website provider with millions of users who sign in and use the services in their accounts such as Netflix, Amazon Prime, etc. But what if multiple users from different countries use the same account and password, this will lead to a huge loss for the company that is providing the services. So, that company wants to avoid this fraudulent behaviour and also have a track of user behaviour such as login – logout, purchase, create and delete the account, settings change, etc.

 

Data Engineering

Image: Architecture for Processing Example taken

The proposed system processes the data using,

  • Apache Kafka – a cluster of connected machines also called a distributed streaming platform.
  • Apache Flink –  a distributed processing engine which helps in the stateful processing of data.

Apache Kafka helps to receive data from various sources and store it reliably also it allows different applications to read the data in the same order in which it was received.

Apache Flink will read the events from the Apache Kafka topic and the process is to identify the fraudulent account and user action logging.

Fraudulent Account Detection:

The account can be considered a fraudulent account based on the account login history. Below are the 2 things which need to be considered while monitoring the login activity.

  1. location
    • If one account is logged in from multiple locations at the same time then that will be considered a fraudulent account.
    • But implementing this concept has more complexity in real-time because the login time recorded is from the system’s internal clock. So, we have to identify the location first and then take the internet time for that location.
  2. Login time
    • Monitoring only the login location will help to identify if the login happens from different countries
    • So, the proposed system also needs to monitor if the same account is logged in from different locations within 5 minutes then that should also be considered fraudulent activity.

So, if the fraudulent activity is found then the alert will be triggered for this event by the alerting system. 

On the other hand, the recorded user actions will be loaded into the Database from the Apache Kafka topic for real-time analysis. So, the action will be taken based on the identified behavior after processing the data in Apache Flink.

Conclusion to Data Engineering 

From the delineated example, we can understand the importance of data engineering to process the data efficiently and in a secure manner. The main role of data engineers is to design the pipeline in a way that we acquire the data without any loss, clean and optimize it then store it properly and make it accessible to the users from disparate divisions across the organization.

Key Takeaways from the article are listed below,

  • Understanding the data engineering pipelines which will explain the steps and various methods involved in acquiring the data, preprocessing and storing it.
  • various methods of processing the data from ancient times to the latest trends.
  • An illustrated simple example of how data engineering is implemented in real-time.
  • An overview of how the Apache Kafka platform and Apache Flink framework have made the data engineering job easier.
  • Understanding the need for data engineering in the industry.

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

P
Pavithra

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