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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
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 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 is Schema in SQL?

ayushi9821704
ayushi9821704 05 Aug, 2024
4 min read

Introduction

Think about a situation where you are drawing a plan of a new structure. While people think about an efficient and robust building’s design when they hear an architect’s plan, a schema in SQL is a blueprint into how data in a database will be arranged. It gives the specification on how the data is going to be stored, how they are going to be linked or associated and how the required data is going to be retrieved without much formalization of the concepts behind this. In this article, you will learn what schema in SQL is, the parts of a schema, and how a schema works in the framework of relational databases, so you can master this concept and appreciate its function within this field.

Schema in SQL

Learning Outcomes

  • Know what a schema is in SQL and why it is used.
  • Find out on the various parts that are involved in the formulation of a schema.
  • Learn about the SQL and its functions such as creating, altering, and dropping of schemas from the database.
  • Explore best practices for designing effective database schemas.
  • Gain insights into common use cases and examples of schemas in real-world applications.

Table of contents

What is a Schema in SQL?

A schema in SQL is a set of structures that contain definitions of objects within a database. It essentially serves as a container for tables, views, indexes, procedures, and such like. It guarantees that data is well stored and this is accompanied with relations between different data sets.

  • Structure: Schemas provide a structured way to organize data, making it easier to manage and query.
  • Security: By grouping related objects, schemas help enforce data security and access controls.
  • Organization: They allow database administrators to separate and manage different parts of the database logically.

Components of a Schema

  • Tables: Store data in rows and columns. Each table represents a specific entity, such as customers or orders.
  • Views: Virtual tables created by querying one or more tables. Views provide a way to present data without altering the underlying tables.
  • Indexes: Improve the speed of data retrieval operations by creating a faster lookup mechanism for table columns.
  • Stored Procedures: Predefined SQL queries or operations that can be executed repeatedly. They help encapsulate logic and improve performance.
  • Constraints: Rules applied to data columns to ensure data integrity. Common constraints include primary keys, foreign keys, and unique constraints.

Creating and Managing Schemas

We will now learn how to create schema and then manage it efficiently.

Creating a Schema

In SQL, schemas can be created using the CREATE SCHEMA statement. For example:

CREATE SCHEMA sales;

This command creates a new schema named “sales.”

Adding Objects to a Schema

Once a schema is created, you can add objects to it. For instance, to create a table within the “sales” schema:

CREATE TABLE sales.customers (
    customer_id INT PRIMARY KEY,
    customer_name VARCHAR(100),
    contact_email VARCHAR(100)
);

Modifying a Schema

To modify an existing schema, such as adding a new table or altering an existing table, you use ALTER statements. For example:

ALTER TABLE sales.customers ADD phone_number VARCHAR(15);

Dropping a Schema

To remove a schema and all its contained objects, use the DROP SCHEMA statement:

DROP SCHEMA sales CASCADE;

The CASCADE keyword ensures that all objects within the schema are also deleted.

Advantages of Using Schemas

  • Improved Organization: Schemas provide a clear structure for organizing database objects, making it easier to manage and locate data.
  • Enhanced Security: By isolating different parts of the database, schemas help enforce access controls and protect sensitive information.
  • Simplified Maintenance: Schemas allow for logical grouping of objects, which simplifies database maintenance and modifications.
  • Efficient Data Management: Clear defined schemas contribute the efficiency of data storage and usage which leads to the enhancement of the performance.
  • Facilitates Development: Schemas help in the development process since different developers can work on different sections of the database at the same time without causing conflicts.

Best Practices for Designing Schemas

  • Normalization: Doing so, measure variance in accordance with normalization principles to minimize redundancy and sharpen up data quality.
  • Naming Conventions: Use standard and meaningful names for the tables, fields and various items used in the database.
  • Documentation: Documenting the schema of the documents and their association for easy inspection and improvements in the future.
  • Indexing: When columns are often used in the search queries then create indexes on those columns to get a better performance.
  • Security: Choose and apply the access control measures and permissions to limit access to a number of data.

Common Challenges with Schemas

  • Complexity: As databases grow, schemas can become complex, making management and understanding more challenging.
  • Scalability: Ensuring that the schema design scales with the growth of data and the organization can be difficult.
  • Migration: Changes to schema design may require careful planning and execution to avoid data loss or corruption.
  • Performance: Inefficient schema design can lead to performance issues, requiring optimization and adjustments.

Conclusion

Schemas are very important in the structure as well as in the administration of data within the SQL database. In this way, schemas help specify the data storage, its retrieval and also provide the means to protect the information. Knowledge on creation, alteration, and administration of schemas is crucial when it comes to establishing an efficient database system. When you are using SQL, knowing about some guidelines on how the schema should be designed also enables one to construct good databases.

Frequently Asked Questions

Q1. What is a schema in SQL?

A. A schema in SQL is a collection of database objects, such as tables, views, and indexes, that defines the structure and organization of data.

Q2. How do I create a schema in SQL?

A. Use the CREATE SCHEMA statement followed by the schema name. For example: CREATE SCHEMA sales;.

Q3. What are the components of a schema?

A. Components include tables, views, indexes, stored procedures, and constraints.

Q4. How can I modify an existing schema?

A. Use ALTER statements to modify existing tables or add new objects within the schema.

Q5. What is the purpose of using schemas?

A. Schemas help organize data, enforce security, and maintain data integrity by providing a structured framework for database objects.

beginnersSQLSQL Schema
ayushi9821704
ayushi9821704 05 Aug, 2024

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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