As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

The statistic displays the most popular SQL databases used by software developers worldwide, as of **********. According to the survey, ** percent of software developers were using MySQL, an open-source relational database management system (RDBMS).

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of *******. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

As of December 2022, relational database management systems (RDBMS) were the most popular type of DBMS, accounting for a ** percent popularity share. The most popular RDBMS in the world has been reported as Oracle, while MySQL and Microsoft SQL server rounded out the top three.

A comparison table of SQL GUI client features across multiple database tools including dbForge Edge, MySQL Workbench, Beekeeper Studio, DBeaver, DataGrip, HeidiSQL, Navicat Premium, DBVisualizer, RazorSQL, and OmniDB.

Cloud SQL Market Outlook

According to our latest research, the global Cloud SQL market size in 2024 stands at USD 7.8 billion, reflecting robust adoption across industries. The market is poised for significant expansion, projected to reach USD 32.5 billion by 2033, growing at a compelling CAGR of 17.2% during the forecast period. This remarkable growth is primarily driven by the increasing demand for scalable, flexible, and cost-efficient database management solutions that support digital transformation initiatives worldwide.

A primary growth factor for the Cloud SQL market is the accelerating shift toward cloud-based infrastructure in organizations of all sizes. Enterprises are increasingly migrating their data workloads to the cloud to leverage benefits such as reduced operational costs, enhanced scalability, and improved data accessibility. Cloud SQL solutions, with their managed database services, eliminate the need for manual database maintenance and updates, thereby allowing IT teams to focus on core business activities. Furthermore, the proliferation of data from IoT devices, mobile applications, and digital services is generating an unprecedented amount of structured and unstructured data, necessitating robust database solutions that can seamlessly scale with demand. As organizations prioritize agility and innovation, the adoption of Cloud SQL platforms is becoming integral to their IT strategies.

Another significant driver is the growing emphasis on data security, compliance, and disaster recovery. Cloud SQL services offer advanced security features, including data encryption, automated backups, and multi-region replication, ensuring business continuity and regulatory compliance. The rise in cyber threats and stringent data protection regulations such as GDPR and HIPAA have made secure data management a top priority for enterprises. By leveraging Cloud SQL, organizations can mitigate the risks associated with data breaches and ensure that their critical business information is protected against potential threats. Additionally, the ability to automate backup and recovery processes reduces downtime and safeguards against data loss, further enhancing the value proposition of cloud-based SQL databases.

The integration of advanced analytics and artificial intelligence is also catalyzing the expansion of the Cloud SQL market. Organizations are increasingly harnessing the power of business intelligence and analytics tools to extract actionable insights from their data. Cloud SQL platforms facilitate seamless integration with analytics solutions, enabling real-time data processing and visualization. This capability is particularly valuable for industries such as retail, healthcare, and BFSI, where timely insights can drive better decision-making and competitive advantage. As digital transformation accelerates, the need for agile, intelligent, and data-driven operations will continue to fuel the adoption of Cloud SQL solutions across diverse sectors.

From a regional perspective, North America currently dominates the Cloud SQL market, accounting for the largest share in 2024, driven by the presence of leading cloud service providers, rapid technological advancements, and high digital adoption rates. Europe follows closely, propelled by stringent data privacy regulations and strong demand from sectors such as BFSI and healthcare. The Asia Pacific region is anticipated to witness the fastest growth, with a CAGR exceeding 19%, fueled by increasing cloud adoption among SMEs, government digitalization initiatives, and a burgeoning IT services sector. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with growing investments in cloud infrastructure and digital transformation projects.

Database Type Analysis

The Cloud SQL market is broadly segmented by database type into relational and non-relational databases. Relational databases, such as MySQL, PostgreSQL, and Microsoft SQL Server, continue to dominate the market due to their widespread use in transactional applications and enterprise workloads. These databases are prized for their ability to maintain data integrity, support complex queries, and provide consistent performance. Businesses in industries like BFSI, healthcare, and retail rely heavily on relational databases for mission-critical applications where data accuracy and reliability are paramount. The demand for managed relational database services in the cloud is further boosted by the need for seamless migration fr

Approximately ** percent of the surveyed software companies in Russia mentioned PostgreSQL, making it the most popular database management system (DBMS) in the period between February and May 2022. MS SQL and MySQL followed, having been mentioned by ** percent and ** percent of respondents, respectively.

https://upload.wikimedia.org/wikipedia/commons/0/08/Netflix_2015_logo.svg" alt="Netflix Logo"> 📝** Dataset Description:**

This dataset contains a collection of Netflix titles including TV shows and movies. It includes details such as title, director, cast, country, date added, release year, rating, duration, genres, and descriptions.

The dataset has been used for performing SQL-based data analysis in MySQL Workbench. The goal of this analysis is to uncover insights such as:

Total number of shows and movies

Top countries producing Netflix content

Most common ratings

Year-wise content addition trends

Frequent directors and actors

Duration-based analysis (e.g., longest shows or movies)

Genre frequency

Content trends in the last 5 years

This dataset is part of a SQL-based Data Analytics Project and is cleaned and structured for immediate use in relational databases.

The Structured Query Language (SQL) server transformation market is experiencing robust growth, projected to reach $15 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.4% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of cloud-based solutions and the rising demand for real-time data analytics are significantly impacting the market. Businesses are increasingly migrating their on-premise SQL servers to cloud platforms like AWS, Azure, and Google Cloud, driven by scalability, cost efficiency, and enhanced accessibility. Furthermore, the growing need for faster data processing and improved database performance is pushing organizations to adopt advanced SQL server technologies, including in-memory databases and distributed SQL solutions. The market is segmented by deployment model (cloud, on-premise), database type (relational, NoSQL), and industry vertical (finance, healthcare, retail). Major players like Oracle, IBM, Microsoft, and Amazon Web Services are actively investing in research and development, launching new products and services to solidify their market positions. Competitive pressures are driving innovation and pushing the market towards more efficient, scalable, and secure solutions. The restraining factors impacting the market include the complexities associated with migrating existing SQL servers to new platforms, the high initial investment required for cloud-based solutions, and security concerns related to data breaches. However, the long-term benefits of improved efficiency, scalability, and cost optimization are outweighing these challenges, leading to sustained market growth. The ongoing trend of big data adoption and the demand for advanced analytics are creating new opportunities for vendors. We anticipate that the market will see increased adoption of serverless SQL databases and the development of more sophisticated tools for data integration and management in the coming years. This will likely reshape the competitive landscape and accelerate the transformation of the SQL server market.

WikiSQL (Questions and SQL Queries)

80654 hand-annotated questions and SQL queries on 24241 Wikipedia tables

By Huggingface Hub [source]

About this dataset

A large crowd-sourced dataset for developing natural language interfaces for relational databases. WikiSQL is a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia.

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to develop natural language interfaces for relational databases. The data fields are the same among all splits, and the file contains information on the phase, question, table, and SQL for each interface

Research Ideas

• This dataset can be used to develop natural language interfaces for relational databases.
• This dataset can be used to develop a knowledge base of common SQL queries.
• This dataset can be used to generate a training set for a neural network that translates natural language into SQL queries

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | phase | The phase of the data collection. (String) | | question | The question asked by the user. (String) | | table | The table containing the data for the question. (String) | | sql | The SQL query corresponding to the question. (String) |

File: train.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | phase | The phase of the data collection. (String) | | question | The question asked by the user. (String) | | table | The table containing the data for the question. (String) | | sql | The SQL query corresponding to the question. (String) |

File: test.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | phase | The phase of the data collection. (String) | | question | The question asked by the user. (String) | | table | The table containing the data for the question. (String) | | sql | The SQL query corresponding to the question. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

Distributed Relational Database Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global Distributed Relational Database Market Drivers

The market drivers for the Distributed Relational Database Market can be influenced by various factors. These may include:

Growing Data Volume: Organizations require scalable and effective methods to handle and process massive amounts of data due to the exponential growth in data generation. Scalability and enhanced performance are two features that make distributed relational databases a good option for managing large amounts of data.

Cloud Adoption: The market for distributed relational databases has been greatly impacted by the emergence of cloud computing. Cloud platforms are encouraging the usage of distributed databases in cloud environments with their scalable infrastructure and managed database services. Distributed databases are also included by cloud providers into their services, increasing accessibility.

Global Distributed Relational Database Market Restraints

Several factors can act as restraints or challenges for the Distributed Relational Database Market. These may include:

Complexity in Management: Complex configurations and management are frequently associated with distributed relational databases. It can be difficult to ensure data consistency, manage distributed transactions, and deal with node failures; these tasks may call for specific knowledge and resources.

High Initial Costs: Including infrastructure investments and licensing fees, the implementation of distributed relational databases might come with a hefty upfront cost. These upfront expenses may prevent adoption in smaller businesses or those with tighter budgets.

A comparison table of popular database documentation tools, including supported DBMS, documentation formats, ease of use, customization options, and pricing.

Each group has processed their data and stored them in NetCDF format following ISMIP6 standards (Seroussi et al., 2020), with ensembles of different sizes for each forcing. In order to efficiently treat the data, we have relied on SQLite, a C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine. SQLite is the most used database engine in the world. Our work with SQLite uses the sqlite3 module that provides a python interface for SQL. The SQL database also outputs simulation results in many different formats, depending on the user's requests. The database as well as CSV files are available here.

view code : https://colab.research.google.com/drive/1rLk-mdsWsdxwQdYYJS24rAP9KABtbiqu?usp=sharing

  Example :





  {"messages": [





  {"role": "system", "content": "You are a SQL expert assistant. Generate clear, efficient SQL queries based on user requests. Provide only the SQL query without any additional text or explanation."}





  {"role": "user", "content": "What are the top 5 most popular genres of music in the database, based on the number of tracks… See the full description on the dataset page: https://huggingface.co/datasets/fknguedia/SQL-GENERATOR-DATASETS.

This dataset is designed for SQL analysis exercises, providing comprehensive data on pizza sales, orders, and customer preferences. It includes details on order quantities, pizza types, and the composition of various pizzas. The dataset is ideal for practicing SQL queries, performing revenue analysis, and understanding customer behavior in the pizza industry.

1. order_details.csv Description: Contains details of each pizza order. Columns: order_details_id: Unique identifier for the order detail. order_id: Identifier for the order. pizza_id: Identifier for the pizza type. quantity: Number of pizzas ordered

2. pizza_types.csv Description: Provides information on different types of pizzas available. Columns: pizza_type_id: Unique identifier for the pizza type. name: Name of the pizza. category: Category of the pizza (e.g., Chicken, Vegetarian). ingredients: List of ingredients used in the pizza.

3. Questions.txt Description: Contains various SQL questions for analyzing the dataset. Contents: Basic: Retrieve the total number of orders placed. Calculate the total revenue generated from pizza sales. Identify the highest-priced pizza. Identify the most common pizza size ordered. List the top 5 most ordered pizza types along with their quantities.

Problem Statement:

Imagine you are working as a data scientist at Zomato. Your goal is to enhance operational efficiency and improve customer satisfaction by analyzing food delivery data. You need to build an interactive Streamlit tool that enables seamless data entry for managing orders, customers, restaurants, and deliveries. The tool should support robust database operations like adding columns or creating new tables dynamically while maintaining compatibility with existing code. ##Business_Use_Cases: Order Management: Identifying peak ordering times and locations. Tracking delayed and canceled deliveries. Customer Analytics: Analyzing customer preferences and order patterns. Identifying top customers based on order frequency and value. Delivery Optimization: Analyzing delivery times and delays to improve logistics. Tracking delivery personnel performance. Restaurant Insights: Evaluating the most popular restaurants and cuisines. Monitoring order values and frequency by restaurant.

#Approach: 1) Dataset Creation: Use Python (Faker) to generate synthetic datasets for customers, orders, restaurants, and deliveries. Populate the SQL database with these datasets. 2) Database Design: Create normalized SQL tables for Customers, Orders, Restaurants, and Deliveries. Ensure compatibility for dynamic schema changes (e.g., adding columns, creating new tables). 3) Data Entry Tool: Develop a Streamlit app for: Adding, updating, and deleting records in the SQL database. Dynamically creating new tables or modifying existing ones. 4) Data Insights: Use SQL queries and Python to extract insights like peak times, delayed deliveries, and customer trends. Visualize the insights in the Streamlit app.(Add on) 5) OOP Implementation: Encapsulate database operations in Python classes. Implement robust and reusable methods for CRUD (Create, Read, Update, Delete) operations. 6) Order Management: Identifying peak ordering times and locations. Tracking delayed and canceled deliveries. 7) Customer Analytics: Analyzing customer preferences and order patterns. Identifying top customers based on order frequency and value.

8) Delivery Optimization: Analyzing delivery times and delays to improve logistics. Tracking delivery personnel performance. 9) Restaurant Insights: Evaluating the most popular restaurants and cuisines. Monitoring order values and frequency by restaurant.

**##Results: ** By the end of this project, learners will achieve: A fully functional SQL database for managing food delivery data. An interactive Streamlit app for data entry and analysis. Should write 20 sql queries and do analysis. Dynamic compatibility with database schema changes. Comprehensive insights into order trends, delivery performance, and customer behavior.

##Project Evaluation metrics: Database Design: Proper normalization of tables and relationships between them. Code Quality: Use of OOP principles to ensure modularity and scalability. Robust error handling for database operations. Streamlit App Functionality: Usability of the interface for data entry and insights. Compatibility with schema changes. Data Insights: Use 20 sql queries for data analysis Documentation: Clear and comprehensive explanation of the code and approach.

SQL Query Audit Tools Market Outlook

According to our latest research, the global SQL Query Audit Tools market size reached USD 1.42 billion in 2024, driven by the surging demand for robust database security and regulatory compliance across industries. The market is projected to grow at a CAGR of 13.8% from 2025 to 2033, reaching an estimated USD 4.13 billion by 2033. This remarkable growth is primarily fueled by the increasing sophistication of cyber threats, the proliferation of data privacy regulations, and the rapid digital transformation initiatives adopted by enterprises worldwide.

One of the most significant growth factors for the SQL Query Audit Tools market is the mounting pressure on organizations to ensure data security and compliance with stringent regulatory frameworks such as GDPR, HIPAA, SOX, and PCI DSS. As data breaches and insider threats become more prevalent, businesses are investing heavily in advanced SQL audit solutions to monitor, detect, and respond to suspicious database activities in real time. These tools provide comprehensive visibility into SQL queries, user access patterns, and configuration changes, thereby enabling organizations to proactively mitigate risks and avoid costly penalties. Furthermore, the increasing adoption of cloud-based databases and hybrid IT environments has created new vulnerabilities, intensifying the need for robust SQL query auditing capabilities across diverse deployment models.

Another key driver propelling the market is the growing emphasis on performance optimization and operational efficiency within database management. Organizations are increasingly leveraging SQL query audit tools not only for security but also for performance monitoring, anomaly detection, and resource utilization analysis. By continuously auditing and analyzing SQL queries, businesses can identify bottlenecks, optimize query execution, and ensure high availability of mission-critical applications. The integration of artificial intelligence and machine learning into these tools further enhances their ability to detect complex threats, automate compliance reporting, and provide actionable insights for database administrators. This convergence of security, compliance, and performance monitoring functionalities is expected to accelerate the adoption of SQL query audit tools across various industry verticals.

The proliferation of digital transformation initiatives across sectors such as BFSI, healthcare, retail, and government is also contributing to the robust growth of the SQL Query Audit Tools market. As organizations migrate to cloud-based infrastructures and embrace data-driven decision-making, the volume, velocity, and variety of data being stored and processed in SQL databases are increasing exponentially. This surge in data complexity necessitates advanced auditing solutions capable of scaling with enterprise needs while maintaining stringent security and compliance standards. Additionally, the rise of remote work and distributed teams has expanded the attack surface, further emphasizing the importance of comprehensive SQL query auditing to safeguard sensitive information and maintain operational resilience.

From a regional perspective, North America currently dominates the SQL Query Audit Tools market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of major technology vendors, early adoption of advanced cybersecurity solutions, and a highly regulated business environment have positioned North America at the forefront of market growth. Meanwhile, Asia Pacific is anticipated to witness the highest CAGR over the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing awareness of data privacy issues among enterprises in emerging economies. The Middle East & Africa and Latin America are also expected to experience steady growth as organizations in these regions prioritize database security and compliance in response to evolving cyber threats and regulatory mandates.

Component Analysis</h2&g

Open source object relational database system that uses and extends SQL language combined with many features that safely store and scale the most complicated data workloads. PostgreSQL runs on all major operating systems.

In-Memory Database Market is Segmented by Processing Type (OLTP, OLAP, and HTAP), Deployment Mode (On-Premise, and More), Data Model (SQL, Nosql, and Multi-Model), Organization Size (SMEs, and Large Enterprises), Application (Real-Time Transaction Processing, and More), End-User Industry (BFSI, Telecommunications and IT, and More), and Geography (North America, Europe, Asia-Pacific, South America, and Middle East and Africa).

This project answers some business questions for a cupcake business company, by analyzing their sales data by SQL. The business wants to know

1. Find the unique flavors.
2. Find the revenue per flavor
3. Total Revenue for the year 2023
4. Which month has the highest sales?
5. which flavor sells most during this month?
6. Which is the most popular flavor?
7. Which flavor has the most rating?
8. Is there any relation between rating 5 and revenue?
9. Top 3 loyal customers
10. From which city are we getting the most orders?

Here the database used is PostgreSQL .