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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

Is manual ETL better than No-Code ETL: Are ETL tools dead?

S
SALONI Last Updated : 19 Apr, 2021
4 min read

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

Introduction

ETL pipelines look different today than they used to decades ago. Much of the architecture and data surrounding ETL have changed. Many companies still believe in creating their own framework for data pipelining and not utilize the existing tools even though that requires a wide range of skills. The traditional way of performing ETL is by doing it manually with code and it is not a single person job. Traditional ETL might be considered a bottleneck, but that doesn’t mean it’s invaluable. Let’s understand the current scenario here!

 

ETL in a nutshell

As data enthusiasts, we all must have frequently come across this term while dealing with data on an everyday basis right? Typically Data engineers handle all this work this but even data analysts, data scientists, and business intelligence engineers are required to have hands-on experience. Not all companies will provide clean data to directly perform analytics and reporting, some will expect to collaboratively work with Data engineers in creating scalable and efficient end-end data pipelines.

ETL or No-Code ETL image
Source: Xplenty.com 

Why is it so hyped? 

Large companies especially hire ETL developers, ETL specialists who only manage data integration and design the data storage for them. The 3 step process looks simple but behind the scenes, there are hundreds of extremely cumbersome things to handle the extraction of data from various sources especially the data itself has become much bigger and messier.

It is the most challenging part of the entire data pipeline and cannot afford to mishandle crucial information before going to the next step. After transforming the clean and validated data into the desired form, it is safe to store it in the data warehouse for analytics and data modeling purposes. Various risks are involved during the initial stages of the production phase and that’s why they are paid more. Knowing the data in and out is the basic foundation for any data-related role and it is mandatory to have a basic idea of using the data smartly with the available resources.

How to perform ETL?

There are different workflows in data pipelining and can be done in 2 ways:

  • ETL tools
  1. Talend
  2. Informatica
  3. Alteryx
  4. SSIS
  5. Amazon Redshift
  6. Xplenty
  7. QlikSense
  • Coding
  1. Python
  2. R
  3. SQL
  4. Java
  5. Apache Pig

No-code ETL vs Manual ETL

A no-code ETL platform requires little to no coding. Tools provide user-friendly GUIs with various functionalities to create a data map. Once the data map is complete, the teams just have to run the process and the server will do its job. The process is easy to understand by the clients and easy to maintain. It is scalable and saves a lot of time and money for the companies handling real-time datasets. The logic is reusable for any data source and there are custom data manipulation features. There are subscriptions and pay-per-use ETL services to run on a cloud server with millions of data. So, the company needs to choose the tool wisely according to the use-case and requirements of the customer.

Even non-technical employers need to be trained to schedule the workflows, jobs, and tasks in order to get acquainted with the tool. There are companies encouraging no-code practices to develop various products.

“According to the IT research firm Forrester, the low-code development platform market will reach a value of $21.2 billion by 2022, growing at an annual rate of 40 percent. What’s more, 45 percent of developers have already used a low-code platform or expect to do so in the near future.” [1]

Coding your own extraction pipeline is tempting but difficult at the same time. Writing python scripts to extract, transform and load data even in a cloud environment is adapted by many companies. Any type of customizations to the code can be made if something is not available in the existing ETL solution. Coding our own ETL can be a huge benefit in terms of performance optimization and flexibility. If there is an expert data engineer on board who knows ETL processes, it is possible to fine-tune the ETL process to run as smoothly as possible. Tedious coding is useful in self-services where one can independently perform data preprocessing.

Issues? Changing the code and maintaining scripts can be a huge problem if the ETL doesn’t function well for complex schemas. Automating the manual ETL requires other tools like Selenium, Windows Task scheduler to automatically run scripts on a daily/weekly basis to store data in excel or a database.  Hence they are built for a specific set of users and data operations.

Do you love playing with dirty data and clean it?

If you like experimenting with data manually by checking all the errors and normalizing the data then implementing various python and R packages is a good way to go. Even writing SQL queries can be interesting and challenging to extract information from messy data. This can help to deeply understand the logic behind data wrangling from scratch rather than starting off with a tool first.

Bottom Line: It all depends on various parameters like the size of data, memory, and budget to choose the optimal solution for business problems. Also, the choice of ETL approach varies with technical and non-technical expertise level in a company.

Final thoughts

This is a quite debatable topic and both have their advantages and disadvantages. ETL tools are not dead but also not preferable by all. One might end up now with an unnecessary overhead of using an ETL tool where it’s not needed, that also hosts business logic that is not transferable outside of the ETL tool. But the skills developed while creating ETL pipelines using Python or SQL will always stick together in coming years.

Current ETL tools might go out of fashion and adapting to new ones can be difficult for some people. So even tools can become a hassle for a company if not utilized properly. Irrespective of no-code or manual ETL, the whole process itself is complicated but also very interesting to learn.

Which one is your favorite? Do let me know in the comments! 

Thank you for reading this article. 

 

Reference

[1] How low-code platforms are transforming software development

About the author:

Saloni Somaiya works as a Data Scientist at a healthcare startup in the United States. She pursued Masters in Information Systems from Northeastern University, Boston. She enjoys reading articles and exploring new technologies. She is willing to contribute more to Data Analytics and Science field. 

LinkedIn: https://www.linkedin.com/in/saloni-somaiya/

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

S
SALONI

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Responses From Readers

Clear

Dev
Dev

Thank you for the post. Coming from a non-data science background but as someone who has heard the terms but never worked in Data Warehousing this was a good comparison and overview of No-code ETL vs Manual ETL.

123

Mary Louise
Mary Louise

or you can combine both approaches, ETL and coding in a tool like Omniscope Evo

123

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