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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

An Introduction on ETL Tools for Beginners

A
ARYA Last Updated : 19 May, 2022
4 min read

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

Introduction on ETL Tools

The amount of data being used or stored in today’s world is extremely huge. Many companies, organizations, and industries store the data and use it as per the requirement. While handling this huge amount of data, one has to follow certain steps. Whenever we start working with data, specific words/terms come to our minds. For example, data warehouses, databases, attributes, ETL, data filtering, etc. In this article, we are going to have a brief introduction to one such term named ETL.

 

What is ETL?

ETL stands for extract, transform, load. Let’s see these terms one by one.

Extract:

It means extracting data from its source which can be an application or another database. Extraction can be divided further into two types:

a) Partial extraction

b) Full extraction

Transform:

It means transforming the raw data which has been extracted from sources. Transforming includes filtering the data, cleaning the data, mapping and transforming data, etc. This step may include some simple changes to source data or some multiprocessing which includes multiple data sources.

Load:

It means converting transformed data into the target database. The target databases can be DataMart, Data Warehouses, or databases. These destination sources are used for analytical purposes, planning business strategies, etc.

In short, the ETL tool performing the above three steps ensures that the data is complete, usable and as per the requirement for further processes like analysis, reporting, and machine learning/artificial intelligence.

Where to use ETL?

Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence include a lot of data. The cloud is the only feasible solution to store this huge amount of data. Besides, both of these techniques require large datastores for analytical model building and training. Cloud-based ETL tools are useful here to both migrate large amounts of data to the cloud and transform them to be analytics-ready.

Data Warehousing

Many of the enterprisers use ETL tools to collect data from various sources, then transform it into a consistent format and load it into a data warehouse. Then business intelligence teams can analyze the data stored in data warehouses for business purposes. Data warehouses play an important role in various business intelligence functions. Also, they act as a key component in creating dashboards/reports.

Data Migration

Data Migration is the process of transferring data from one system to another while changing the storage, database, or application. ETL plays an important role here. ETL tools help in integrating the contextual data which can be further used by business analysts/marketers for personalized marketing, improving the user experience, or in understanding customer behavior.

Why use ETL?

There are plenty of reasons why ETL is being used. ETL provides a method of moving data from various sources into a data warehouse. It helps companies to analyze their business data and further helps in making critical business decisions or planning marketing strategies. Sample data comparison can be performed between the source and target systems with the help of ETL. ETL offers deep historical context as well, which can be used for various business purposes. Besides, ETL helps to migrate the data into a data warehouse.

ETL Pipeline

ETL Pipeline is a set of processes that are used to extract the data from source/multiple sources, transforming it and then loading it into the target sources. The target sources can be Datamart, data warehouses, or simple databases. This stored data is further used for analysis, data insights, reporting, or data synchronization. The main purpose of ETL Pipeline is to make data useful for business intelligence. An ETL Pipeline is useful for making the data available to marketers or decision-makers by centralizing it. Also, it helps in standardizing this data. The important use of ETL Pipeline is in data migration i.e., it helps in migrating the data from legacy systems to data warehouses. For best results, the ETL pipeline should provide continuous data processing. To gain more advantages, ETL should be such that it will increase data access.

ETL Challenges

Loss of Data/Irrelevant data

There is a possibility that some of the data is lost or data gets corrupted because some steps are not performed correctly while transforming or loading the data. Some irrelevant data can also be there due to such mistakes.

Disparate Data Sources

Sometimes the data sources may not be aligned or mapped properly. In such cases, dealing with these data sources becomes a big challenge.

Problems with data quality and integrity

Sometimes while normalizing or transforming the data, there can be performance issues. This may lead to loss of data quality or data integrity. Hence, it becomes another big challenge while using ETL.

ETL Tools

ETL Tools can be of different types. Some software companies develop and sell commercial ETL software products. They can be included in Enterprise Software ETL Tools. Examples of such tools are as follows:

1. SAP Data Services

2. Oracle Data Integrator

3. IBM ETL Tool

4. SAS Data Manager

Another type of ETL tool is open-source ETL tools. For example, Hadoop. Hadoop is a general-purpose distributed computing platform. It can be used to store, manipulate and analyze data. These products are free to use.

The Third type of ETL Tool is Custom ETL Tools. These are simple programming languages that are being used by many companies to write their own ETL tools. These programming languages include Python, Java, SQL, Spark, and Hadoop. These types of ETL tools provide the greatest flexibility. Although, they require a lot of effort.

Apart from these tools, Amazon AWS, Google Cloud Platform, and Microsoft Azure provide their own ETL capabilities as cloud services.

Conclusion

ETL model is being used by many companies for more than 30 years. Many companies read data from various sources, transform this extracted data using different techniques and then load it into the destination sources/systems. Though, some challenges to be faced while using/testing ETL tools, the ETL Tools are in use for many years. Companies use ETL to safely move their data from one system to another.

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

A
ARYA

I Arya Talathi, am currently a Computer Engineering Student, who likes to seek out new challenges; the one who finds pleasure in exploring new things.

A passionate learner who loves problem-solving, especially when it is a Data problem. Being a Data Science and Machine Learning enthusiast; always ready to play with the data and come to conclusions. Besides, invests a good amount of time in Competitive Programming and Web Designing.

I like to read and write. No amount of money can replace the kind of happiness and satisfaction I derive from writing and exploring the Data Science field. I try to share my knowledge through my writing because I believe in "Knowledge increases by sharing but not by saving".

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