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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

Delta Lake: A Comprehensive Introduction

S
Siddharth Sonkar Last Updated : 02 Jan, 2023
4 min read

Introduction

Delta Lake is an open-source storage layer that brings data lakes to the world of Apache Spark. Delta Lakes provides an ACID transaction–compliant and cloud–native platform on top of cloud object stores such as Amazon S3, Microsoft Azure Storage, and Google Cloud Storage.

It enables organizations to quickly and reliably build data lakes on cloud object stores. It provides an intuitive, cloud–native platform for data engineers, scientists, and analysts to explore, discover, and build data-driven applications collaboratively.

Delta Lake

Delta Lakes makes it easy to store and query data in a format compatible with the open-source Lakehouse data model. It provides a comprehensive set of features, including data versioning, audit logging, and fine–grained access control. Let’s see what makes delta lakes so special. Let’s understand its features and working. So, let’s get started.

Features of Delta Lake

Delta lake was created by the original creators of Apache Spark and was designed to provide the benefits of both worlds: the transaction’s ability of databases with the horizontal scalability of data lakes.

  1. ACID Transactions: It provide ACID transactions to ensure data consistency on reads and writes. It also supports snapshot isolation for reads to ensure readers have a consistent data view.
  2. Schema Enforcement: It enforces schema on reads and writes. This ensures data quality and integrity by catching early issues such as type mismatches and missing fields.
  3. Versioning: It supports data versioning, which helps track the changes made to the data. This allows you to build reliable pipelines that make use of data.
  1. Time Travel: It supports rich data access by allowing users to query the data at any time. This helps in debugging and auditing the data pipelines.
  1. Scalability: It is designed to scale to petabytes of data. It supports parallel reads and writes by using Apache Spark.
  1. Open Source: It is an open-source project. It is available on GitHub and also supports other open-source technologies such as Apache Spark.

Now am sure most of you might be thinking that if delta lake is such a powerhouse packed with such features, then without any doubt, most companies might want to convert their data lake to delta lake.

Top 5 Reasons to Use Delta Lake

Delta Lake

 

1. Scalable: It provides storage for large amounts of data and is highly scalable. It can easily handle batch and streaming data and can process data from multiple sources.

2. Cost-effective: It is cost–effective for both storage and processing. It is open source, making it free to use, and the underlying technology optimizes storage costs.

3. High performance: It provides high performance with low latency, allowing users to access data quickly and efficiently. 4. Data reliability: Delta Lake helps ensure data reliability by providing atomic transactions, which guarantee that changes are applied in an atomic manner and can be rolled back if needed.

5. Data governance: It provides tools for data governance, allowing users to track changes and manage data lineage. This helps to ensure data is consistent, secure, and compliant.

If you have an existing data lake and have considered the above reasons and want to transition to using Delta Lake, you can do so by following these steps:

Data Lake to Delta Lake- Understanding the Transition

Delta Lake

1. Understand the existing data lake architecture:

The first step is to understand the existing data lake architecture. This will help to identify the existing data sources and their corresponding data sets and storage formats.

2. Evaluate the existing data lake architecture:

The next step is to evaluate the existing data lake architecture. This will help to identify the areas of improvement, scalability, and security.

3. Design the Delta Lake Architecture:

The next step is to design the Architecture. This will involve designing the data models, data pipelines and other components needed to deploy the Delta Lake.

4. Implement the Delta Lake Architecture:

The next step is to implement the  Architecture. This will involve setting up the data lake, configuring data pipelines, and deploying the Delta Lake components.

5. Test the Delta Lake Architecture:

The next step is to test the  Architecture. This will involve validating the data pipelines, data models, and other components of Delta Lake.

6. Migrate Data to Delta Lake:

The final step is to migrate the data from the existing data lake to Delta Lake. This will involve transforming the data, migrating it to the Delta Lake, and validating the data in the new data lake

Conclusion

In summary, DeltaLake is an open–source storage layer that provides reliable data management and unified analytics on data stored in data lakes. It enables organizations to manage their data lakes in a way that is secure, reliable, and compliant with regulations. It also provides scalability, ACID transactions, and support for multiple languages. Finally, it provides integration with cloud data warehouses and data lakes, as well as with Apache Spark and Apache Kafka.

S
Siddharth Sonkar

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