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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

Introduction to Azure Data Lake Storage Gen2

Kishan Yadav
Kishan Yadav Last Updated : 03 Jun, 2022
6 min read

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

Introduction ADLS Gen2

The ADLS Gen2 service is built upon Azure Storage as its foundation. It combines the capabilities of ADLS Gen1 with Azure Blob Storage. Azure Data Lake Storage is capable of storing large quantities of structured, semi-structured, and unstructured data in their original file formats. For example, it can store Text files, CSV files, JSON files, XML files, images, videos, etc. When the uploading of files gets completed after that we can use any technologies like Databricks, or Hadoop, to process and analyze the data as per our business needs.

Data Lake Storage Gen2 makes Azure Storage the inspiration for building enterprise knowledge lakes on Azure Cloud. it’s designed to service multiple petabytes of data while sustaining many gigabits of turnout, Data Lake Storage Gen2 helps you to simply manage huge amounts of data.

A fundamental part of Data Lake Storage Gen2 is the addition of a hierarchical namespace to Blob storage. The hierarchical namespace organizes objects/files into a hierarchy of directories for efficient data access.

In this article, we will explore Azure Data Lake Storage Gen2 (ADLS Gen2) service. We are going to create a storage account using an azure free tier subscription and use Microsoft Azure Storage Explorer to create storage containers and upload the data inside the containers from our local system.

Steps to Create ADLS Gen2 Storage Account

Pre-Requisite:-

Need to have at least an Azure free tier subscription. I will use a free tier subscription to perform the below steps.

Step 1 – We will open the Azure portal (portal.azure.com). This is the home page of the Azure cloud.

Azure Data Lake Storage

Step 2 – Now we will create the storage service.

Step 2.1 – To create an Azure Storage Account, first click on the “create a resource” icon from the azure services to create your service.

Azure Data Lake Storage

Step 2.2 – Type “storage account” and select the only option “storage account” to create this service.

Azure Data Lake Storage

Step 2.3 – Now, click on “Create” button.

Azure Data Lake Storage

Step 2.4 – Provide the following information in the “Basics” tab of the “Create a storage account” page –

“Project Details” Section –

    • Subscription – Select the proper “Subscription” to use from the Dropdown.
    • Resource Group – Select “RG-Storage” from the Dropdown of “Resource Group”.
Azure Data Lake Storage

          “Instance Details” Section –

    • Storage account name – Type the name “blogdemostg ” in the provided input box.
    • Region – Select the default option “East US” from the Dropdown of “Region”. If you want you can change it as per your choice.
    • Performance – Select the option “Standard: Recommended for most scenarios (general-purpose v2 account)”.
    • Redundancy – Select the “Locally-redundant storage(LRS)” Option in the Dropdown.
Azure Data Lake Storage

Step 2.5 – Now, in advance option go to Data Lake Storage Gen2 section and here to “Enable hierarchical namespace” click on the checkbox.

Step 2.6 – Finally, click on the “Review + create” button.

Azure Data Lake Storage

Step 2.7 – Once, the message “Validation passed” is displayed, click on the “Create” button.

Step 3 – Now our service got created and now you will be redirected to the storage account deployment page. Here, click on the “Go to resource” button.

Step 4 – Finally our service gets launched and we can see all the credentials that we have defined during the creation of the ADLS Gen2 storage account.

Step 5 – Finally, we verify our storage service in Microsoft Azure Storage Explorer. It will help us to create containers, and upload data files and folders from our local systems to our storage account which we have created in Azure Cloud. Install it locally and you can access all the storage-related functionality of Azure from yo.  You can download it from the storage account page or just click here.

Step 6 – Now open the Microsoft Azure Storage Explorer and connect it with your Azure account. To connect, just click on the “Plug” symbol provided on the left side of the menu options and then click on the “Subscription” section, and then it will redirect you to connect your Microsoft account. Fill in the details and your azure account will get connected with storage explorer. Now you can see your storage service name inside your storage accounts dropdown.

Uploading Data in ADLS Gen2

We have successfully created our first ADLS Gen2 storage account. Now, we will upload the data inside it using Microsoft Azure Storage Explorer. To upload the data we are going to create a folder with the name “raw”. Inside this folder, we are going to upload our data. Let’s go…

Steps to upload the Data in ADLS Gen2 Storage Accounts

Step 1 –  To create a container or folder, right-click on “Blob Containers” which are inside your storage account. Now a pop will appears and here right-click on “Create Blob Container”.

Step 2 – Type the name of your folder inside the provided box as “raw”.

Step 3 – Click on the raw folder and you will see different options, click on the “upload” option. Here you will get 2 options whether you want to upload a file or you want to upload a folder. We will upload a file to our storage folder.

Step 4 – Click to upload files option and a file upload prompt will open, there you can select the file to upload. After selecting click on the “upload button” and the file gets uploaded into the raw container. I am uploading the scraped data “CSV file” named “inshort_news_data_7.csv” to our storage. You can see the below image.

Step 5 – Now your file gets uploaded into the Azure storage. You can check the “Activities” section which will display the status of your task, and whether your task is successful or gets failed.

Conclusion

In this article, we have discussed important steps one needs to know while creating their first Data Lake Storage Service in Azure Cloud.

This article has covered the following topics:

  • Details about ADLS Gen2 Storge Service.
  • Steps to create your first Storage Account in Azure Cloud.
  • Connecting your Azure account with Microsoft Azure Storage Explorer.
  • Creating containers in your ADLS Gen2 storage account with the help of Microsoft Azure Storage Explorer.
  • Uploading the data files and folders inside your containers from your local system.

I hope you liked my article on ADLS Gen2. If you have any opinions or questions, then comment down below. Connect with me on LinkedIn for further discussion.

Happy Learning!

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

Kishan Yadav
Kishan Yadav

Hello 👋,
I am a Data Engineer with a proven track record of working in the information technology and services industry. I am skilled in Apache Spark, Hive, SQL, Python, Hadoop, Databricks and Cloud.

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