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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

Data Warehouses, Data Marts and Data Lakes

Harshit Ahluwalia
Harshit Ahluwalia Last Updated : 07 Jan, 2022
4 min read

Introduction

All data mining repositories have a similar purpose: to onboard data for reporting intents, analysis purposes, and delivering insights. By their definition, the types of data it stores and how it can be accessible to users differ. This article will discuss some of the features and applications of data warehouses, data marts, and data lakes.

 

What is a Datawarehouse?

A Datawarehouse works as a central repository that merges information from different sources and consolidates it through the extract, transform and load process, also known as ETL process, into a comprehensive database used for analytical and business techniques. At a very high level, the ETL process extracts the data from multiple sources, transforms it into a cleaned format to be used for business processes, and finally loads that data into the data repository. A data warehouse stores current data and the historical information that has been cleansed, conform categorized. When data gets loaded into the data warehouse, it is modelled and structured, ready for a specific purpose. Moreover, a data warehouse was traditionally used for storing data from transactional databases such as CRM, ERP, HR and Finance applications. But with the advancement in technology like NoSQL technologies and new data sources, non-relational databases are also used for data warehousing. Typically a data warehouse has a 3-tier architecture.

Datawarehouse

The bottom tier of the architecture includes the database servers, which could be relational or non-relational or maybe both, that extract data from multiple sources and consolidate it into one.

The second tier of this architecture includes OLAP Server, a software category that allows users to proceanalyzeanalyze data/information coming from multiple databases server. The topmost level of this architecture is the Client Front end layer. This layer includes all the applications and tools used for reporting, queryianalyzingalyzing data. In response to the rapid growth and today’s sophisticated analytical tools, data warehouses that once resided in on-premise centres are moving towards the cloud. Compared to an on-premise data warehouse, a cloud warehouse provides many benefits that include lower cost, limitless storage and computer capabilities, scale on a pay as you go basis, faster disaster recovery. Organization station, you should opt for a cloud-based data warehouse as it has many benefits compared to the on-premises data warehouse. Popularly used data warehouses include Teradata, Oracle Exadata, IBM DB2 Warehouse on the cloud, Amazon Redshift, Big Query by Google Cloudera’s Enterprise Datahub and Snowflake Cloud Data warehouse.

Companies using Data warehouse

Read more about Data warehouse on our blog.

What are Data Marts?

The data mart is a subsection of the data warehouse built for a particular operational task or a business function, purpose, or community of users. There are specific reasons for creating data marts; the first is that we can easily access frequent data. Secondly, end-user response time is improved. Thirdly, easy creation of data mart, because for creating the data warehouse a lot of resources and work is to be done while the creation of data mart is far easy compared to the data warehouse. There is a minimal cost that is related to data mart.

There are three types of data marts: dependent, independent, and hybrid. Dependent data marts are built by drawing data from the existing central warehouse. In contrast, independent data marts are created by drawing from operational or external data sources or both. Dependent data mart offers analytical capabilities for a restricted data warehouse area. Moreover, it also provides isolated security and solo performance. A hybrid data mart combines input from a data warehouse, operational systems and external systems. The difference lies in how the data is extracted from the source, how data has been transformed that needs to be applied, and how the data has been transported into the mart. Dependent Data Mart pulls data from the enterprise data warehouse, which has already been cleaned and transformed. Independent data mart has to carry out the cleansing and transformation of data as the data has been coming from the operational systems and external sources. Whatever the type, the main aim of the data mart is to provide data to the end-users that are most relevant to them when they need it. These data marts accelerate the business process and offer a cost and time-efficient way to take the data-driven decision.

What is a Data Lake?

A Data Lake is a repository of data that stores all types of data, whether structured, unstructured, or semi-structured. It holds a large amount of data in its native format.

Data Lake

Source: Image

While a data warehouse stores data that has been adequately cleaned, transformed and ready for business operations or analytical tasks. While dumping the data into a data lake, data can be loaded without defining the structure and schema of the data. In simpler terms, we can say that a data lake is a repository that contains raw data in its native form, straight from the source. It does not mean that a data lake is a place to dump your data without governance. While the data is appropriately classified in the data lake, it is protected and governed. Data lakes can be deployed using cloud object storage such as Amazon S3 or large scale distributed systems such as Apache Hadoop to process big data. They can also be deployed on a relational database management system or NoSQL data repositories. Data lakes offer many benefits, such as they can store all types of data such as structural, unstructured and semi-structured data. The second benefit is saving time in defining the data’s structure, schemas, and transformations as it is imported into the data lake in its raw format. Vendors that provide technologies, platforms, and reference architectures for data lakes include Amazon, Cloudera, Google, IBM, Informatica, Microsoft, Oracle, SAS, Snowflake, Teradata, and Zaloni.

 

End Notes

This article taught us data mining repositories’ capabilities, such as data warehouses, data marts, and data lakes. While they all have a similar goal, they need to be evaluated within the use case and technology infrastructure to select the best one for the organization’s needs.

 

Harshit Ahluwalia
Harshit Ahluwalia

Growth Hacker | Generative AI | LLMs | RAGs | FineTuning | 62K+ Followers https://www.linkedin.com/in/harshit-ahluwalia/ https://www.linkedin.com/in/harshit-ahluwalia/ https://www.linkedin.com/in/harshit-ahluwalia/

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