This is the sample database from sqlservertutorial.net. This is a great dataset for learning SQL and practicing querying relational databases.

Database Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4146319%2Fc5838eb006bab3938ad94de02f58c6c1%2FSQL-Server-Sample-Database.png?generation=1692609884383007&alt=media" alt="">

Terms of Use

The sample database is copyrighted and cannot be used for commercial purposes. For example, it cannot be used for the following but is not limited to the purposes: - Selling - Including in paid courses

Publicly accessible databases often impose query limits or require registration. Even when I maintain public and limit-free APIs, I never wanted to host a public database because I tend to think that the connection strings are a problem for the user.

I’ve decided to host different light/medium size by using PostgreSQL, MySQL and SQL Server backends (in strict descending order of preference!).

Why 3 database backends? I think there are a ton of small edge cases when moving between DB back ends and so testing lots with live databases is quite valuable. With this resource you can benchmark speed, compression, and DDL types.

Please send me a tweet if you need the connection strings for your lectures or workshops. My Twitter username is @pachamaltese. See the SQL dumps on each section to have the data locally.

Dataset

This dataset was created by Mandeeph

Released under Database: Open Database, Contents: Database Contents

Contents

This dataset has been uploaded to Kaggle on the occasion of solving questions of the 365 Data Science • Practice Exams: SQL curriculum, a set of free resources designed to help test and elevate data science skills. The dataset consists of a synthetic, relational collection of data structured to simulate common employee and organizational data scenarios, ideal for practicing SQL queries and data analysis skills in a People Analytics context.

The dataset contains the following tables:

departments.csv: List of all company departments. dept_emp.csv: Historical and current assignments of employees to departments. dept_manager.csv: Historical and current assignments of employees as department managers. employees.csv: Core employee demographic information. employees.db: A SQLite database containing all the relational tables from the CSV files. salaries.csv: Historical salary records for employees. titles.csv: Historical job titles held by employees.

Usage

The dataset is ideal for practicing SQL queries and data analysis skills in a People Analytics context. It serves applications on both general Data Analytics, and also Time Series Analysis.

A practical application is presented on the 🎓 365DS Practice Exams • SQL notebook, which covers in detail answers to the questions of SQL Practice Exams 1, 2, and 3 on the 365DS platform, especially ilustrating the usage and the value of SQL procedures and functions.

Acknowledgements & Data Origin

This dataset has a rich lineage, originating from academic research and evolving through various formats to its current relational structure:

Original Authors

The foundational dataset was authored by Prof. Dr. Fusheng Wang 🔗 (then a PhD student at the University of California, Los Angeles - UCLA) and his advisor, Prof. Dr. Carlo Zaniolo 🔗 (UCLA). This work is primarily described in their paper:

• Wang, F., & Zaniolo, C. (2004). Publishing and Querying the Histories of Archived Relational Databases in XML. DOI:10.1109/WISE.2003.1254473.

Relational Conversion

It was originally distributed as an .xml file. Giuseppe Maxia (known as @datacharmer on GitHub🔗 and LinkedIn🔗, as well as here on Kaggle) converted it into its relational form and subsequently distributed it as a .sql file, making it accessible for relational database use.

Kaggle Upload

This .sql version was then loaded to Kaggle as the « Employees Dataset » by Mirza Huzaifa🔗 on February 5th, 2023.

This data contains Create database, Use, create table (int, varchar, date), describe, alter table (add, modify, char, varchar, after, rename column, to, drop column, drop), show tables, Rename table (to), Drop table.

Blockchain data query: SQL Practice

Dataset

This dataset was created by vishnusan

Released under Apache 2.0

Contents

Dataset

This dataset was created by Austin Oberg

Contents

Example code list definition in csv format.

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

Dataset

This dataset was created by Edwin_Lee_Tiño

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Andrew Dolcimascolo-Garrett

Released under MIT

Contents

Available functions in rEHR.

This set comprises the data collected to inform the Discussion Paper: Advanced Experiences in Cybersecurity Policies and Practices- an Overview of Estonia, Israel, South Korea and the United States, result of the collaboration between Dr. James A. Lewis from the Center for Strategic International Studies (CSIS) and the Inter-American Development Bank (IDB). The data is based on the Cybersecurity Capability Maturity Model (CMM) jointly developed by the Organization of American States (OAS), the Inter-American Development Bank and the Global Cyber Security Capacity Center (GCSCC) at the University of Oxford and it supports the structured assessment of these four countries cybersecurity experiences and the lessons learned derived from them. This data set compliments the 2016 Cyber Security Report Data Set published in April 2016.

Click here to access the data: https://mydata.iadb.org/d/a9yc-jpsa

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Managed MySQL Services Market Outlook

According to our latest research, the global managed MySQL services market size reached USD 5.8 billion in 2024 and is projected to grow at a CAGR of 17.2% through the forecast period, reaching USD 27.3 billion by 2033. This strong growth trajectory is primarily driven by the increasing demand for scalable, secure, and cost-effective database management solutions across a diverse array of industries. The proliferation of data-driven applications, coupled with the shift toward cloud-first strategies and digital transformation initiatives, is accelerating the adoption of managed MySQL services worldwide.

One of the key growth factors propelling the managed MySQL services market is the rapid expansion of enterprise data volumes and the corresponding need for robust database management. As organizations generate and process vast amounts of structured and unstructured data, the complexity of maintaining high availability, reliability, and performance for mission-critical databases increases. Managed MySQL services provide enterprises with expert administration, automated backups, and proactive performance monitoring, allowing IT teams to focus on core business activities while ensuring optimal database operations. Furthermore, the rise of DevOps practices and agile development methodologies necessitates seamless database provisioning and management, which managed MySQL services are uniquely positioned to deliver.

Another significant driver is the growing emphasis on data security, compliance, and risk mitigation. With stringent regulatory requirements such as GDPR, HIPAA, and PCI DSS, organizations are under pressure to ensure the confidentiality, integrity, and availability of their data assets. Managed MySQL service providers offer advanced security features, including encryption, access controls, vulnerability assessments, and continuous monitoring, thereby helping enterprises address compliance mandates and protect against evolving cyber threats. The increasing frequency of data breaches and ransomware attacks further underscores the importance of partnering with trusted managed service providers to safeguard sensitive information.

The shift toward cloud-based infrastructure and the adoption of hybrid and multi-cloud strategies are also fueling market growth. Cloud deployment models offer unparalleled scalability, flexibility, and cost efficiency, enabling organizations to dynamically adjust resources based on workload demands. Managed MySQL services delivered via the cloud eliminate the need for capital-intensive hardware investments and reduce operational overhead, making them particularly attractive to small and medium enterprises (SMEs) as well as large organizations with distributed operations. The seamless integration of managed MySQL services with leading cloud platforms, such as AWS, Azure, and Google Cloud, further enhances their value proposition by supporting global reach and rapid deployment.

In addition to the growing demand for managed MySQL services, the market is also witnessing a surge in interest for Managed YugabyteDB Services. YugabyteDB, an open-source distributed SQL database, is gaining traction due to its ability to handle high transaction volumes and provide strong consistency across distributed environments. As organizations increasingly adopt microservices architectures and cloud-native applications, the need for databases that can seamlessly scale and integrate with these modern infrastructures becomes critical. Managed YugabyteDB Services offer enterprises the flexibility to deploy and manage their databases across multiple cloud platforms, ensuring high availability and resilience. This capability is particularly appealing to businesses looking to enhance their digital transformation initiatives while maintaining robust data management practices.

From a regional perspective, North America currently dominates the managed MySQL services market, accounting for the largest share in 2024. This leadership is attributed to the region's advanced IT ecosystem, high rate of cloud adoption, and the presence of major managed service providers. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by digital transformation initiatives, increasing investments in cloud infrastructure, and the rapid expans

SQL-Verified Generation Market Outlook

According to our latest research, the global SQL-Verified Generation market size reached USD 2.18 billion in 2024, driven by the increasing demand for data integrity, compliance, and advanced analytics across industries. The market is expected to grow at a robust CAGR of 13.5% from 2025 to 2033, reaching a projected value of USD 6.47 billion by 2033. This growth is primarily attributed to the surge in data-driven decision-making, the proliferation of digital transformation initiatives, and the rising regulatory requirements for data validation and security worldwide. As organizations continue to prioritize accurate, reliable, and compliant data operations, the adoption of SQL-Verified Generation solutions is set to accelerate steadily over the forecast period.

One of the primary growth factors propelling the SQL-Verified Generation market is the exponential increase in data volumes generated by enterprises across various sectors. As organizations collect and process vast amounts of structured and unstructured data, the need for robust SQL-based verification tools becomes critical to ensure data accuracy, consistency, and reliability. These solutions enable businesses to automate data validation processes, minimize human errors, and enhance the overall quality of business intelligence outputs. Furthermore, the integration of artificial intelligence and machine learning with SQL-Verified Generation platforms is enabling advanced analytics and real-time data validation, further amplifying market growth. The ongoing digital transformation across industries such as BFSI, healthcare, and retail is pushing organizations to invest in scalable and secure data management solutions, thereby fueling the demand for SQL-Verified Generation systems.

Another significant driver for market expansion is the stringent regulatory landscape governing data privacy, security, and compliance. With regulations such as GDPR, HIPAA, and CCPA becoming more rigorous, organizations are under increasing pressure to ensure that their data processing and storage practices are fully compliant. SQL-Verified Generation solutions play a vital role in automating compliance checks, auditing data flows, and providing transparent reporting mechanisms. These capabilities not only help organizations avoid hefty fines and reputational damage but also build trust with stakeholders and customers. The growing emphasis on data governance and risk management is prompting enterprises to adopt advanced SQL-Verified Generation tools that offer comprehensive compliance and auditing functionalities, thereby contributing to sustained market growth.

Technological advancements and the shift towards cloud-based infrastructures are also catalyzing the adoption of SQL-Verified Generation solutions. Cloud deployment offers unparalleled scalability, flexibility, and cost-effectiveness, making it an attractive option for organizations of all sizes. The increasing integration of SQL-Verified Generation tools with cloud-based platforms and enterprise resource planning (ERP) systems is streamlining data integration, validation, and reporting processes. This seamless integration is not only enhancing operational efficiency but also enabling organizations to leverage real-time insights for strategic decision-making. As cloud adoption continues to rise, especially among small and medium enterprises, the market for SQL-Verified Generation solutions is poised for significant expansion in the coming years.

From a regional perspective, North America and Europe are currently leading the SQL-Verified Generation market, driven by the presence of major technology providers, high digital adoption rates, and strict regulatory frameworks. However, the Asia Pacific region is emerging as a lucrative market, fueled by rapid digitalization, increasing investments in IT infrastructure, and a growing focus on data-driven business strategies. Countries such as China, India, and Japan are witnessing substantial growth in sectors like BFSI, healthcare, and e-commerce, which is boosting the demand for advanced data validation and compliance solutions. As organizations in emerging economies continue to embrace digital transformation, the SQL-Verified Generation market is expected to witness robust growth across all major regions.

Blue/Green for Databases Market Outlook

According to our latest research, the Global Blue/Green for Databases market size was valued at $1.12 billion in 2024 and is projected to reach $4.98 billion by 2033, expanding at a robust CAGR of 18.4% during the forecast period 2025–2033. The primary driver fueling this remarkable growth is the escalating demand for continuous deployment and zero-downtime database operations across diverse industry verticals. As organizations increasingly prioritize seamless user experiences and operational resilience, the adoption of Blue/Green deployment strategies for databases is rapidly becoming a best practice, ensuring business continuity and minimizing the risks associated with database upgrades and maintenance.

Regional Outlook

North America currently commands the largest share of the Blue/Green for Databases market, accounting for over 38% of global revenues in 2024. This dominance is primarily attributed to the region’s mature IT infrastructure, heightened awareness of advanced DevOps practices, and early adoption of automation technologies among large enterprises. The United States, in particular, stands out due to its concentration of technology innovators, robust cloud service ecosystems, and stringent data management regulations that prioritize uptime and transactional integrity. Strategic investments by leading cloud providers, coupled with a highly skilled workforce and a culture of digital transformation, have further entrenched North America’s leadership in the global Blue/Green for Databases market.

The Asia Pacific region is poised to be the fastest-growing market, projected to register a CAGR of 22.7% between 2025 and 2033. This rapid expansion is driven by accelerated digitalization initiatives, surging cloud adoption, and substantial investments in IT modernization across China, India, Japan, and Southeast Asia. Enterprises in these countries are increasingly recognizing the value of Blue/Green deployment models for minimizing service interruptions and enhancing customer satisfaction. Government-led digital transformation programs, coupled with the proliferation of e-commerce and fintech startups, are catalyzing the adoption of advanced database deployment strategies. Additionally, the presence of global cloud vendors and regional technology service providers is fostering a highly competitive and innovative ecosystem in Asia Pacific.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing Blue/Green deployment for databases, albeit at a more measured pace due to infrastructural and skillset limitations. Localized demand is being fueled by the growing penetration of digital banking, e-commerce, and government modernization projects. However, challenges such as limited access to skilled DevOps professionals, legacy system constraints, and intermittent connectivity issues are impeding widespread adoption. Policy reforms aimed at digital transformation and data sovereignty are expected to gradually mitigate these barriers, paving the way for increased uptake of Blue/Green for Databases solutions in these regions over the forecast period.

Report Scope

SQL program. Program written in SQL performing the six queries on the MySQL database. (SQL 15.3 kb)

This is a beginner-friendly SQLite database designed to help users practice SQL and relational database concepts. The dataset represents a basic business model inspired by NVIDIA and includes interconnected tables covering essential aspects like products, customers, sales, suppliers, employees, and projects. It's perfect for anyone new to SQL or data analytics who wants to learn and experiment with structured data.

Tables and Their Contents:

Products:

Includes details of 15 products (e.g., GPUs, AI accelerators). Attributes: product_id, product_name, category, release_date, price.

Customers:

Lists 20 fictional customers with their industry and contact information. Attributes: customer_id, customer_name, industry, contact_email, contact_phone.

Sales:

Contains 100 sales records tied to products and customers. Attributes: sale_id, product_id, customer_id, sale_date, region, quantity_sold, revenue.

Suppliers:

Features 50 suppliers and the materials they provide. Attributes: supplier_id, supplier_name, material_supplied, contact_email.

Supply Chain:

Tracks materials supplied to produce products, proportional to sales. Attributes: supply_chain_id, supplier_id, product_id, supply_date, quantity_supplied.

Departments:

Lists 5 departments within the business. Attributes: department_id, department_name, location.

Employees:

Contains data on 30 employees and their roles in different departments. Attributes: employee_id, first_name, last_name, department_id, hire_date, salary.

Projects:

Describes 10 projects handled by different departments. Attributes: project_id, project_name, department_id, start_date, end_date, budget.

Why Use This Dataset?

• Perfect for Beginners: The dataset is simple and easy to understand.
• Interconnected Tables: Provides a basic introduction to relational database concepts like joins and foreign keys.
• SQL Practice: Run basic queries, filter data, and perform simple aggregations or calculations.
• Learning Tool: Great for small projects and understanding business datasets.

Potential Use Cases:

• Practice SQL queries (SELECT, INSERT, UPDATE, DELETE, JOIN).
• Understand how to design and query relational databases.
• Analyze basic sales and supply chain data for patterns and trends.
• Learn how to use databases in analytics tools like Excel, Power BI, or Tableau.

Data Size:

Number of Tables: 8 Total Rows: Around 230 across all tables, ensuring quick queries and easy exploration.