Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

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 SQLite?

ayushi9821704
ayushi9821704 12 Aug, 2024
5 min read

Introduction

Just think that you can find a fast and simple database engine that has no configuration and can be installed directly into the applications and grants high-level SQL support without a requirement for creating a server. This one is widely utilized in applications and web browsers due to its simplicity, high-level performance, and simplicity of implementation. In this article, you will be taken through what SQLite entails, its operations, the benefit that relates to its usage and the procedure to be followed so as to use the SQLite.

Learning Outcomes

  • Understand what SQLite is and its key features.
  • Learn the advantages and limitations of using SQLite.
  • Know how to set up and use SQLite in your projects.
  • Explore common use cases and applications for SQLite.
  • Gain insights into SQLite’s architecture and file format.
  • Be able to execute basic SQLite commands and queries.

Table of contents

What is SQLite?

SQLite is a C-language library that implements a small, fast, self-contained, high-reliability, full-featured SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. It reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views is contained in a single disk file.

Key Features of SQLite

  • Self-Contained: SQLite is a single library that requires minimal setup.
  • Zero Configuration: No setup or administration required.
  • Serverless: It is not stand-alone server that is why it is integrated into the application .
  • Cross-Platform: Developed for Windows, macOS , Linux and mobile platforms and operating systems such as iOS and Android.
  • Full SQL Support: Provides most of the utilities of the SQL standard such as using query, transaction, and sub-query.
  • Reliable and Fast: Known for its reliability and performance in both read and write operations.

Advantages of SQLite

  • Simplicity: Easy to integrate and use.
  • Lightweight: Small footprint, ideal for mobile and embedded applications.
  • Flexibility: Suitable for development state and production state equally.
  • Cost-Effective: Non proprietory software which can be downloaded and used by anyone under a common license.
  • ACID Compliance: Ensures data integrity and reliability.

Limitations of SQLite

  • Concurrency: Limited support for concurrent writes.
  • Scalability: Not suitable for high-volume, high-throughput applications.
  • Features: Lacks some advanced features of other RDBMS like stored procedures.

Setting Up SQLite

Getting started with SQLite is straightforward. Here’s a simple guide to setting it up and running your first query.

Installation

  • Download SQLite: Obtain the appropriate binary files from the official SQLite website.
  • Install SQLite: Follow the instructions for your operating system to install SQLite.
SQLite

Basic Usage

Let us now look into the basic usage of SQLite.

Creating a Database

To create a new SQLite database, you use the command:

sqlite3 mydatabase.db

This command initializes a new SQLite database file named mydatabase.db. If the file does not already exist, SQLite creates it. If it does exist, SQLite opens the existing file, allowing you to interact with it. This database file will store all your tables, indices, and data.

Creating a Table

Once you get your data base up and running, the next thing need is to create tables that helps you store your data base. You use the SQL CREATE TABLE statement to define a table’s structure: 

CREATE TABLE users (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    email TEXT NOT NULL UNIQUE
);

Inserting Data

After creating a table, you can add data using the INSERT INTO statement:

INSERT INTO users (name, email) VALUES ('Alice', '[email protected]');

Querying Data

To retrieve data from a table, you use the SELECT statement:

SELECT * FROM users;

Common Use Cases for SQLite

  • Mobile Applications: Frequently used in mobile apps for data storage (e.g., Android and iOS).
  • Web Browsers: Used by web browsers to store data locally.
  • Embedded Systems: Ideal for IoT devices and other embedded systems.
  • Development and Testing: Used as a lightweight database during development and testing.

SQLite Architecture and File Format

SQLite has no unnecessary features; it has a straightforward structure and is implemented to be as easy to use and fast as possible. Database is defined as the set of definitions, tables, indexes, and data in one cross platform file on the host computer. One of SQLite’s biggest hallmarks is dynamic typing, which means that all columns of all tables can contain data of any type despite their declared type.

Advanced SQLite Features

Let us now look into some advance features of SQLite below:

Transactions

SQLite supports transactions, which are essential for ensuring data integrity. Transactions allow you to group multiple SQL operations into a single unit of work. This ensures that either all operations are completed successfully or none are applied, maintaining consistency in case of errors.

BEGIN TRANSACTION;
INSERT INTO users (name, email) VALUES ('Bob', '[email protected]');
UPDATE users SET email = '[email protected]' WHERE name = 'Bob';
COMMIT;

In this example, if any part of the transaction fails, you can roll back to the original state, ensuring that partial changes do not corrupt the database.

Indexes

Indexes improve the speed of data retrieval operations on a table by creating a separate data structure to quickly locate records. They are crucial for optimizing queries, especially on large datasets.

CREATE INDEX idx_email ON users(email);

This command creates an index on the email column of the users table. Queries that filter or sort by email will benefit from faster execution due to this index.

Views

Views are virtual tables based on the result of a query. They simplify complex queries by encapsulating them into a reusable, named object. Views do not store data themselves but present data from one or more tables.

CREATE VIEW user_emails AS
SELECT name, email FROM users;

This creates a view called user_emails that provides a simplified way to query user names and emails. You can use this view in the same way as a regular table in SELECT queries.

Triggers

Triggers are automatic actions performed in response to certain events on a table, such as INSERT, UPDATE, or DELETE. They help maintain data integrity and enforce business rules.

CREATE TRIGGER update_timestamp
AFTER UPDATE ON users
FOR EACH ROW
BEGIN
    UPDATE users SET last_modified = CURRENT_TIMESTAMP WHERE id = OLD.id;
END;

This trigger automatically updates the last_modified column with the current timestamp whenever a row in the users table is updated.

Full-Text Search (FTS)

SQLite supports full-text search capabilities using virtual tables. FTS allows efficient searching within large text fields and supports complex queries like phrase searches and ranking.

CREATE VIRTUAL TABLE documents USING fts4(content);
INSERT INTO documents (content) VALUES ('This is a test document.');
SELECT * FROM documents WHERE content MATCH 'test';

This creates a virtual table documents for full-text search on the content column and allows you to search for documents containing the word “test”.

Conclusion

Some might argue that SQLite is an effective, flexible database engine that can be used in many different applications. It has no configuration, which makes it appealing for beginners with negligible ability for complex program configurations by advanced developers. Whether you are developing a mobile application or deciding which type of database to use for web applications, a standalone application, or an embedded system, SQLite provides an optimal solution with high performance, giving almost the same functionality as full-featured DB server or system.

Frequently Asked Questions

Q1. What is SQLite used for?

A. SQLite is used for local storage in applications, ranging from mobile apps and desktop software to embedded systems and web browsers.

Q2. How is SQLite different from other SQL databases?

A. Unlike most SQL databases, SQLite does not require a separate server process, making it serverless and easy to embed within applications.

Q3. Can SQLite handle multiple users?

A. SQLite supports concurrent reads, but concurrent writes are limited, making it less suitable for high-concurrency applications.

Q4. Is SQLite suitable for production use?

A. Yes, SQLite is used in production environments, especially where a lightweight, low-maintenance database is needed.

Q5. How do you back up an SQLite database?

A. Backing up an SQLite database is as simple as copying the database file to a backup location.

databaseserverSQLSQLite
ayushi9821704
ayushi9821704 12 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.

Free Courses

image.name 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
image.name 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
image.name 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
image.name 4.8

Building Your first RAG System using LlamaIndex

Build your first RAG model with LlamaIndex in this free course. Dive into Retrieval-Augmented Generation now!
image.name 4.7

Building Production Ready RAG systems using LlamaIndex

Learn Retrieval-Augmented Generation (RAG): learn how it works, the RAG framework, and use LlamaIndex for advanced systems.

Recommended Articles

Frequently Asked Questions

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Rahul Shah img

Rahul Shah

27
Sion Chakrabarti img

Sion Chakrabarti

16
CHIRAG GOYAL img

CHIRAG GOYAL

87
kaustubh gupta img

kaustubh gupta

24
Barney Darlington img

Barney Darlington

5
Suvojit Hore img

Suvojit Hore

9
Arnab Mondal img

Arnab Mondal

15