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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
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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

What are the differences between Data Lake and Data Warehouse?

Lakshay arora
Lakshay arora Last Updated : 20 Nov, 2020
4 min read

Overview

  • Understand the meaning of data lake and data warehouse
  • We will see what are the key differences between Data Warehouse and Data Lake
  • Understand which one is better for the organization

 

Introduction

From processing to storing, every aspect of data has become important for an organization just due to the sheer volume of data we produce in this era. When it comes to storing big data you might have come across the terms with Data Lake and Data Warehouse. These are the 2 most popular options for storing big data.

data warehouse data lake

Having been in the data industry for a long time, I can vouch for the fact that a data warehouse and data lake are 2 different things. Yet I see many people using them interchangeably. As a data engineer understanding data lake and data warehouse along with its differences and usage are very crucial as then only will you understand if data lake fits your organization or data warehouse?

So in this article, let satiate your curiosity by explaining what data lake and warehousing are and highlight the difference between them.

 

Table of Contents

  1. What is a Data Lake?
  2. What is a Data Warehouse?
  3. What are the differences between Data Lake and Data Warehouse?
  4. Data lake or Data Warehouse: Which one to use?

 

What is a Data Lake?

A Data Lake is a common repository that is capable to store a huge amount of data without maintaining any specified structure of the data. You can store data whose purpose may or may not yet be defined. Its purposes include- building dashboards, machine learning, or real-time analytics.

data lake

Now, when you store a huge amount of data at a single place from multiple sources, it is important that it should be in a usable form. It should have some rules and regulations so as to maintain data security and data accessibility.

Otherwise, only the team who designed the data lake knows how to access a particular type of data. Without proper information, it would be very difficult to distinguish between the data you want and the data you are retrieving. So it is important that your data lake does not turn into a data swamp.

data warehouse or data swamp

Image Source: here

 

What is a Data Warehouse?

A Data Warehouse is another database that only stores the pre-processed data. Here the structure of the data is well-defined, optimized for SQL queries, and ready to be used for analytics purposes. Some of the other names of the Data Warehouse are Business Intelligence Solution and Decision Support System.

 

What are the differences between Data Lake and Data Warehouse?

Data Lake Data Warehouse
Data Storage and Quality The Data Lake captures all types of data like structure, unstructured in their raw format. It contains the data which might be useful in some current use-case and also that is likely to be used in the future. It contains only high-quality data that is already pre-processed and ready to be used by the team.
Purpose The purpose of the Data Lake is not fixed. Sometimes organizations have a future use-case in mind. Its general uses include data discovery, user profiling, and machine learning. The data warehouse has data that has already been designed for some use-case. Its uses include Business Intelligence, Visualizations, and Batch Reporting.
Users Data Scientists use data lakes to find out the patterns and useful information that can help businesses. Business Analysts use data warehouses to create visualizations and reports.
Pricing It is comparatively low-cost storage as we do not give much attention to storing in the structured format. Storing data is a bit costlier and also a time-consuming process.

Data lake or Data Warehouse: Which one to use?

We have seen what are the differences between a data lake and a data warehouse. Now, we will see which one should we use?

If your organization deals with healthcare or social media, the data you capture will be mostly unstructured (documents, images). The volume of structured data is very less. So here, the data lake is a good fit as it can handle both types of data and will give more flexibility for analysis.

If your online business is divided into multiple pillars, you obviously want to get summarized dashboards of all of them. The data warehouses will be helpful in this case in making informed decisions. It will maintain the data quality, consistency, and accuracy of the data.

Most of the time organizations use a combination of both. They do the data exploration and analysis over the data lake and move the rich data to the data warehouses for quick and advance reporting.

data warehouse

 

End Notes

In this article, we have seen the differences between data lake and data warehouse on the basis of data storage, purpose to use, which one to use. Understanding this concept will help the big data engineer choose the right data storage mechanism and thus optimize the cost and processes of the organization.

The following are some additional data engineering resources that I strongly recommend you go through-

If you find this article informative, then please share it with your friends and comment below your queries and feedback.

 

 

 

 

Lakshay arora
Lakshay arora

Ideas have always excited me. The fact that we could dream of something and bring it to reality fascinates me. Computer Science provides me a window to do exactly that. I love programming and use it to solve problems and a beginner in the field of Data Science.

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