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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

Apache Airflow used for Performing ETL

Gitesh Dhore
Gitesh Dhore Last Updated : 28 Jul, 2022
6 min read

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

Introduction

Organizations with a separate transactional database and data warehouse typically have many data engineering activities. For example, they extract, transform and load data from various sources into their data warehouse. Sources include customer transactions, data from Software as a Service (SAAS) offerings, and third-party data that could be useful for analyzing customer behavior. Such ETL jobs are managed using ETL frameworks that help organize appointments into ordered workflow graphs, track them, and monitor service level agreements.
Airflow
Image Source – airflow.apache.org
Airflow has excellent scheduling capabilities, and a graph-based execution flow makes it a great alternative for running ETL jobs. This post will help you learn the basics of Airflow and perform an ETL job to transfer data from Amazon S3 to Redshift.

Apache Airflow

The flagship project of the Apache Software Foundation has already inspired many companies. The highly scalable solution makes the platform suitable for any size of company, from startups to large corporations.
Airflow

Image source – DataValley.com

 

Workflows are defined, planned and executed using simple Python codes. Even complex data channels with numerous internal dependencies between tasks in the workflow are defined quickly and robustly. Because data engineering challenges do not end there, Airflow brings a rich command-line interface, an extensive web interface and an extended REST API, starting with the new major version of Apache-Airflow 2.0. Several features make monitoring and troubleshooting much more accessible. Airflow can be extended with numerous plugins, macros and user-defined classes depending on the requirements.
The status of workflow runs and associated log files are available with a click. Important meta data such as interval and time of the last run are visible in the main view. While Apache Airflow ETL workflows run independently, you are well informed about the current status. However, viewing the web interface is not mandatory, as Airflow optionally sends a notification via email or Slack in case of a failed attempt. The application spectrum focuses on the ETL area, where Apache Airflow machine learning workflows are also optimally coordinated.
Airflow

Image source – apache.airflow.com

 

Apache Airflow web interface ETL Apache Airflow web interface. The status of workflow runs is visible on the left (Running).
The status of the most recent workflow tasks is visible on the right (Recent Tasks).
In the second workflow (example_complex), the context menu is activated.
To get a better overview of the possible use cases of Apache Airflow, we have summarized different use cases for you. In addition to classic application ideas, here you will find inspiration on how to enrich your data channels with state-of-the-art functions.
  • Classical
  • Ambitious
  • (I’m)possible

Classical

The basic idea of ​​workflow management systems is buried in classic use cases: Tasks – especially in ETL – should be performed reliably and on time. In addition, external and internal dependencies must be considered before running the job. For example, an external dependency is a file to process that the sales department provides. Internal dependencies describe a conditional sequence of tasks between each other.
  • Cron alternative
Cron jobs are often used when tasks such as cleaning the database, sending newsletters or evaluating log files are performed regularly. If these tasks are taken into Airflow instead, you stay on top of many jobs and benefit from the traceability of status information.
  • Conditional, modular workflows
Complex dependencies can be implemented easily with Airflow. Integrated concepts such as branching and Task Groups make it low maintenance possible to create complex workflows. Branching allows conditional execution of multiple alternatives, and Task Groups allow smaller parts of a workflow to be reused.
classical | Airflow

Image source – DataValley.com

 

  • Data processing directly after generation
When several systems work together, waiting situations often occur. For example, a Bash task should run directly after sending a file. However, the exact delivery time is unknown. Therefore, airflow will pause the workflow with the sensor job class until this file exists.

Ambitious

The more workflows run on Airflow, the faster ideas for particular use cases emerge. As a power user, you want to go beyond Apache Airflow’s best practices and fully utilize its capabilities. Some advanced use cases only require customizing the configuration file. Others are implemented using custom plugins and task types (operators) or macros. Overall, there are many ways to adjust airflow to meet emerging needs.
  • Flexible working procedures
Although many aspects of the workflow are predefined in Python files, some flexibility can be built into some areas. Context variables such as execution date are available at runtime. This can be used in SQL statements, for example. Additional (encrypted) variables can be created using the CLI, API or web GUI. This can be used, for example, to make file paths flexible for uploads.
ambitious

Image source – DataValley.com

 

Last but not least, the workflow structure itself can be dynamic. For example, the number of job instances for a variable number of records in the database can be set using variables. Likewise, dynamic_component_# components are created using a variable in graphics.
  • Big Data – Horizontal Scaling with Airflow
Horizontally scaled Airflow can process up to 10,000 automated tasks per day. At the same time, several terabytes flow through the data channels every day. For example, Spark transformations and data warehouse downloads are managed by Airflow in the Big Data space. With Airflow, execution errors and inconsistencies are reliably detected, and a response can be implemented as quickly as possible.
For example, suppose the data scheme changes unannounced or the volume of data increases due to exceptional circumstances. In that case, the result is visible in the monitoring, and the notification of responsible persons can be automated.
  • REST API
As a unique feature, Airflow includes an extensive REST API. This can be used to get information about the status of workflow runs, to pause or start workflows, and to store variables and connection information in the backend. Context variables can optionally be passed when the workflow is activated via the interface. Authentication ensures security here. Airflow can also be connected to SAP BW via REST API.

(I’m)possible

Airflow limitations must also be considered to judge which application ideas are possible and which are not. At its core, Airflow is a large-scale batch scheduler mainly used to manage third-party systems. In the basic idea of ​​work procedures
• Workflows without repair plans
Executing a workflow on multiple arbitrary dates cannot be set in motion without additional effort. The web interface and CLI do not offer any functionality for this. The solution provides a REST API or manual workflow execution at the desired time via a web interface.
• Data exchange between workflows
One of the primary applications is the design of data pipelines. With XCOM, at least the metadata is exchangeable between functions. Here, for example, the location of data in the cloud can be handed over to a downstream task. The maximum metadata size is currently limited to the size of a BINARY LARGE OBJECT (BLOB) in the metadata database.

Conclusion

The mentioned points are often the reason for own plug-ins and macros to extend the functionality of Airflow. However, to date, Airflow has proven to be a reliable and well-designed workflow management platform that addresses many challenges of modern data engineering. Airflow is also ideal for training machine learning models coupled with the Anaconda environment.
  • Airflow brings a rich command-line interface, an extensive web interface and an extended REST API, starting with the new major version of Apache-Airflow 2.0.
  • Complex dependencies can be implemented easily with Airflow. Integrated concepts such as branching and Task Groups make it low maintenance possible to create complex workflows.
  •  With Airflow, execution errors and inconsistencies are reliably detected, and a response can be implemented as quickly as possible.

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Gitesh Dhore
Gitesh Dhore

I am a Machine Learning Enthusiast. Done some Industry level projects on Data Science and Machine Learning. Have Certifications in Python and ML from trusted sources like data camp and Skills vertex. My Goal in life is to perceive a career in Data Industry.

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