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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

Difference Between ETL and ELT Pipelines

Kusuma Bhutanadhu
Kusuma Bhutanadhu Last Updated : 17 Mar, 2023
9 min read

Introduction

The data integration techniques ETL (Extract, Transform, Load) and ELT pipelines (Extract, Load, Transform) are both used to transfer data from one system to another.

The information is taken from one or more data sources, transformed into a target system-compatible format, and then loaded into the target system as part of the ETL process. An ETL tool or platform that organizes the process often does this task. To meet the needs of the target system, data must be cleansed, validated, integrated, and enhanced during the transformation stage.

The ELT pipeline, on the other hand, entails removing data from one or more data sources, bringing it into the destination system, and then changing it there. Database management solutions that can process huge volumes of data, such as SQL or Apache Spark, can be used for this operation. This method is advantageous when the data source needs to be kept in its original format, and the target system can make the required transformations.

ETL and ETL Pipeline
                                                         Source: Analytics Vidhya

ETL and ELT pipelines integrate data from various systems, including databases, applications, and files, to generate a consistent and uniform view of the data for analysis, reporting, and decision-making.

Learning Objectives

By the end of this article, you will be able to learn the differences between ETL and ELT Pipelines, their pros and cons, and their application in different cases such as Data Warehousing, Business Intelligence, Data Integration, etc. One would also learn about some methods for designing successful ETL/ELT pipelines, how tools such as Talend, Apache Nifi, Apache Spark, etc can be used, and what strategies for Monitoring and Troubleshooting could be used for ETL and ELT Pipeline.

Table of Contents

Differences Between ETL and ELT Pipelines

ETL (EXTRACTION, TRANSFORMATION, AND LOAD) ELT (EXTRACTION, LOAD, AND TRANSFORMATION)
The information is first extracted from source systems, converted into a format that the target system can use, and then put into the target system. The information is first loaded data into the destination system before the necessary changes are made to the data there.
ETL pipelines might be more helpful when data needs to be transformed into a format that the target system does not natively support. This leads to a lengthy conversion time and more hardware. ELT works by splitting the work into smaller batches and using parallel processing, thus operating more quickly.
The scalability of ETL can be constrained because it alters data before it is put into a target system. They are more scalable as it involves loading data into a target system, which is then transformed using distributed computing tools like Hadoop or Spark.
They are typically easier to maintain as it enables greater control over data consistency and quality, which can help bring down the chance of errors and make pipeline maintenance simpler over time. While using ELT pipelines, it may be more challenging to help find issues and maintain the pipeline because data is loaded into the target system before being transformed.
It uses more affordable than proprietary ETL systems, such as Hadoop and Spark, which help lower processing costs. They are more expensive in terms of cost as it uses open-source technologies.

In conclusion, the choice between ETL and ELT pipelines is based on the particular demands of the data integration project, such as the features of the source and target systems, the volume and complexity of the data, and the requirements for performance and scalability.

Pros and Cons of ETL and ELT Pipelines

In simple and plain language, the advantages and disadvantages of ETL and ELT pipelines for various use cases are as follows:

Benefits of ETL 

  • When information needs to be converted into a format that the target system does not natively support, ETL is helpful.
  • ETL can combine data from many sources into a single picture for evaluation and decision-making.
  • ETL pipelines can be made to run more efficiently by using batch and parallel processing.

Limitations of ETL

  • ETL can be laborious and demand a lot of hardware resources, especially for sophisticated transformations and significant data volumes.
  • To create and apply the transformation logic, ETL can need specialist talents.
  • From the time that information is collected, and the time it is available for analysis, ETL processes may cause delays.

Benefits of ELT 

  • When the target system can handle and transform the data in its native format, ELT can be carried out more efficiently and quickly.
  • When data must be kept in its original form and merely changed for analysis and reporting, ELT is helpful.
  • ELT can use the flexibility and computing capacity of modern database management systems.

Limitations of ELT

  • Before performing transformations, ELT may need much storage space to keep the data.
  • Writing complicated SQL queries may require detailed information for data transformations using ELT.
  • The target system may become more complex due to ELT, making maintenance and upkeep more difficult.

Application of ETL Pipelines

Applications for data warehousing and data analytics frequently employ ETL pipelines. In simple and plain language, the following are some use cases for ETL pipelines:

  • Data Warehousing: In a data warehouse, ETL combines data from several sources into a unified view. The data is cleaned, put into a standard format, and checked to guarantee quality and consistency. ETL frequently loads data into the data warehouse to maintain the data current.
  • Business Intelligence: ETL extracts information from transactional systems and loads it into a data warehouse or data mart in business intelligence applications. To enable reporting and analysis, the information is transformed and consolidated. Collecting, processing, and loading the data into the reporting system is automated and scheduled using ETL.
  • Data Integration: To combine information from different sources into a single system, ETL is employed. One example is data fusion from many databases, spreadsheets, and files. ETL is used to ensure data is accurate and uniform and to change the data into a format that the destination system can use.
  • Data Migration: To transfer information from one system to another, ETL is utilized in data migration projects. This can involve data transfers from an outdated system to a new one or combining data from various systems. ETL is used during the migration process to transform and check the information.

Application of ELT pipelines

ELT (Extract, Load, Transform) processes are widely used in data processing and analysis to prepare vast volumes of data for later use. The following are some simplified usage cases:

  • Data Warehousing: ELT pipelines are commonly used in data warehousing to extract data from various sources, including databases, cloud storage, and online APIs. After being transformed, the data is placed into a database system for additional analysis.
  • Big Data Processing: Analyzing massive amounts of data, including such data streams or log files, uses ELT pipelines in particular. The information is first retrieved and placed into a distributed database, such as Hadoop, before being parallelized using Spark or Hive, among other tools.
  • Machine Learning: Data processing for machine learning applications can be done via ELT pipelines. To do this, data must be gathered from various sources, cleaned up, and transformed to be ready for modeling, then loaded into a framework for machine learning like TensorFlow or PyTorch.
  • Business Intelligence: To provide dashboards and reports for business users, ELT pipelines can collect and transform data from various sources, including customer data, sales data, and web analytics.

Techniques for Designing an Efficient ETL or ELT Pipelines

Extraction, Load, and Transform, or ETL, pipeline design ensures that data processing tasks are finished quickly and accurately. The following are some methods for designing successful ETL/ELT pipelines:

  • Data Partitioning: Data partitioning divides big datasets into more manageable portions for parallel processing. By limiting the quantity of data that needs to be processed at once, partitioning can help increase the speed and efficiency of data processing.
  • Data Cleansing: Data cleaning entails locating and resolving flaws or inconsistencies in the data. Techniques for cleaning data may involve
    • Deleting duplicate or irrelevant information
    • Correcting spelling or grammatical issues
    • Ensuring data consistency across various sources
  • Data Transformation: Data transformation is changing the format or organization of data. Varying data types, combining or merging databases, and collecting or evaluating information are some examples of data transformation procedures.
  • Incremental Loading:  Using total loading, just the data that has changed or been added since the previous processing run is processed. Particularly for large datasets, incremental loading can aid in reducing the amount of time and resources required for data processing.
  • Job Scheduling: Based on variables, including available data, processing time, and available resources, this process entails creating an efficient timetable for performing ETL/ELT processes. An efficient program can shorten the total processing time, guaranteeing timely and correct data processing.

Comparison Between ETL and ELT Tools

Software programs called ETL and ELT tools are used for data integration, processing, and transformation. The following is a simplified comparison of some well-known ETL and ELT tools:

  • Talend: Talend is an open-source data integration and transformation platform that offers a variety of connectors and elements for data transformation. It provides a simple user interface and supports both drag-and-drop and programming methods. It gives capabilities relevant to the development and information quality checks and supports batch and real-time processing.
  • Informatica: A advanced analytical integration and transformation operation can be supported by the commercial ETL product Informatica. It provides a visual development platform and many connectors for different data sources. Coherence offers features like data governance, data manipulation, and data integrity.
  • Apache NiFi: Apache NiFi is an open-source ELT tool on data flow management called Apache NiFi. It includes a range of data intake, transformation, and routing processors and offers a web-based user interface. Data provenance and lineage characteristics are provided by Apache NiFi, which also offers real-time data processing.
  • Apache Spark: ETL and ELT may be performed using Apache Spark, an open and distributed computing technology. It allows bulk and actual data processing and offers fast data processing. Spark offers machine learning, graph analytics, and broadcasting features and supports various programming languages.

Role of Data Integration and Data Quality

ETL and ETL Pipelines
                                                           Source: Masterdata

Combining data from several sources to present a uniform picture of the data is known as data integration. Data from many sources is connected using ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines to create a data warehouse or data lake.

The quality of the data measures data accuracy, wholeness, consistency, and validity. Data quality is essential in setting ETL and ELT pipelines to guarantee that the integrated data is safe and practical.

To put it another way, data quality is like verifying sure the puzzle things fit together correctly and aren’t damaged or missing. In contrast, data integration is like doing the parts of a puzzle together to view the entire picture. The processes that assemble the puzzle pieces and ensure they are oriented and placed in the proper sequence are known as ETL and ELT pipelines.

Strategies for Monitoring and Troubleshooting

It’s essential to track and fix problems with ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines to guarantee the effective and efficient operation of the data integration process. Here are some methods for keeping a watch on things and troubleshooting:

  • Early Warning Issues: Set up warnings and messages to receive notifications when a pipeline fails or encounters issues. This enables you to address problems as soon as they arise and resolve them before they worsen.
  • Monitor pipeline performance: Monitoring pipeline performance involves keeping tabs on parameters, including data processing time, data transfer rates, and resource usage. This can help in pipeline optimization and the detection of obstacles.
  • Log pipeline activities: Log pipeline operations to track data integration development and identify problems or errors. Moreover, logs may be used for compliance and auditing purposes.
  • Conduct regular testing: Test regularly to ensure the pipeline operates as it should. This can assist you in spotting problems before they become expensive downtime.
  • Collaborate with stakeholders: Work together with stakeholders: To find and fix problems, work together with stakeholders like data scientists, engineers, and business users. You can solve issues and correctly understand the information management process as a result.

Conclusion

A project’s particular demands and requirements will determine whether to use ETL or ETL pipeline. ETL works well for small-scale projects requiring manual customization and intervention at each workflow stage. On the other hand, an ETL pipeline is better suited to massive projects with enormous amounts of data that need automation and standardization to ensure correctness and efficiency.

In conclusion, ETL and ETL pipeline are two related but distinct concepts. An ETL pipeline is an automated workflow that controls the entire ETL process from beginning to end. ETL is a three-stage data integration process. The project’s size, complexity, and demand for customization and automation will determine the best option.

ETL and ETL Pipelines
                                                     Source: YouTube.com

Key Takeaways

  • Initially, we have seen an Overview of the differences between ETL and ELT pipelines, including data processing order and performance implications.
  • And then understanding the pros and cons of ETL and ELT pipelines for different use cases.
  • Also, the techniques for designing an efficient ETL or ELT pipeline include data partitioning, data cleansing, and data transformation.
  • Comparison of popular ETL and ELT tools, including Talend, Informatica, Apache NiFi, and Apache Spark.
  • Understanding the role of data integration and data quality in ETL and ELT pipelines.
  • Strategies for monitoring and troubleshooting ETL and ELT pipelines and Comparison of both.
Kusuma Bhutanadhu
Kusuma Bhutanadhu

This is Kusuma. I completed my B-tech in Computer Science Engineering. I like to explore new technologies and techniques. I am interested in computer software fields. I am good at communication and organizational skills

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