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 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.

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

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.

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.

The SQL In-Memory Database market is booming, projected to reach $5556.3 million by 2025 with a 19.1% CAGR. Discover key drivers, trends, and regional insights into this rapidly expanding sector, dominated by major players like Microsoft, IBM, and Oracle. Learn more about in-memory database solutions for real-time analytics and transaction processing.

Descripción de la base de datos

Para este dataset se cuenta con 5 tablas. Los productos son 20 en total, cada uno tiene un stock máximo que se elige de forma aleatoria entre 50 y 120 unidades, un precio inicial que se elije entre 1500 y 3000 y un promedio de compras de .2 a .7.

El precio de los productos puede variar con 10% de incremento, decremento o 0% con la misma probabilidad.

Cada día la tienda puede hacer entre 3 y 8 facturas, y por cada factura un cliente puede comprar entre 1 y 5 productos, y por producto una cantidad que sigue a distribución binomial

Limites de las fechas: 2015/01/01 - 2024/12/31

Tenga en cuenta que los datos se generaron con estas simples reglas, por lo cual es normal encontrar datos que se comporten de forma extraña, este dataset está pensado para practicar.

Diccionario de datos

PRODUCTO

PRECIO

STOCK

FACTURA

FACT_PROD

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

The NewSQL Database market is booming, projected to reach $50 billion by 2033 with an 18% CAGR. Discover key trends, drivers, and leading companies shaping this rapidly evolving sector. Learn about market segmentation and regional insights for informed strategic decision-making.

The booming enterprise database market, valued at $80 billion in 2025, is projected to experience a 12% CAGR through 2033. Learn about key market drivers, trends, and leading companies shaping this dynamic landscape of relational and non-relational databases across cloud and on-premise deployments. Explore regional market analysis for North America, Europe, and Asia-Pacific.

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.

According to Cognitive Market Research, the global Managed Database Service Market is Driven by the need for operational simplification, faster developer velocity, and the complexity of modern data architectures Market Dynamics of Managed Database Service Market

Key Drivers of Managed Database Service Market

Cloud adoption and data proliferation: The rapid shift to cloud-native architectures and exponential data growth (telemetry, clickstreams, application logs) are driving demand for managed databases that scale elastically.

Developer productivity and platformization: Organizations prefer abstracting database operations so engineering teams can focus on application logic — MDS enables faster provisioning, CI/CD integration, and developer self-service.

Complexity of modern data stacks: Polyglot persistence, real-time streaming, multi-region replication, and GDPR-style data rules increase operational complexity, making MDS attractive for enterprises lacking deep in-house DBA skills.

Cost predictability and OPEX preference: Moving from CAPEX-heavy database hardware and internal DBA teams to subscription/usage-based managed services aligns with OPEX budgeting and reduces upfront costs.

Focus on reliability and SLAs: Critical applications require high-availability, automatic failover, and rigorous recovery point/objectives (RPO/RTO) that many MDS providers guarantee through SLAs and runbook-driven operations.

(Source:https://learn.microsoft.com/en-us/azure/reliability/reliability-sql-managed-instance)

Challenges in Managed Database Service Market

Data sovereignty and residency requirements: Strict local regulations in certain jurisdictions require specialized architectures or on-prem/private cloud deployments, complicating standardized MDS offerings.

Vendor lock-in and migration risk: Enterprises worry about portability between managed database platforms, export formats, and cost of moving large datasets between clouds.

Performance predictability: Multitenant DBaaS and noisy-neighbor effects can complicate guaranteed performance for latency-sensitive workloads.

Security and compliance overhead: Managed providers must maintain strong encryption, key management, audit trails, and support for industry-specific compliance (HIPAA, PCI DSS, FINRA) — gaps here slow procurement.

Skill gap for cloud-native DB architectures: Even with MDS, customers require architects who understand distributed databases, eventual consistency trade-offs, and query optimization across new engines.

(Source:https://www.getrightdata.com/blog/modern-data-stack-complexities) Introduction of Managed Database Service Market

The global Managed Database Services (MDS) market provides outsourced administration, monitoring, optimization, backup, recovery, scaling, security, and lifecycle management for databases hosted in cloud, on-premises, or hybrid environments. MDS providers deliver services across relational (e.g., PostgreSQL, MySQL, Oracle, SQL Server), NoSQL (e.g., MongoDB, Cassandra, Redis), and cloud-native data stores (e.g., Amazon Aurora, Google Cloud Spanner). Offerings range from fully managed, multi-tenant database-as-a-service (DBaaS) platforms to managed services for customer-owned database instances, including 24/7 operations, performance tuning, patching, migration, high-availability configuration, disaster recovery, and compliance support.

Deployment models include public-cloud native managed databases (provider-hosted DBaaS), private managed database instances (hosted on customer or partner infrastructure), and hybrid managed database arrangements that combine on-premise data sovereignty with cloud scalability. Managed Database Services are used by enterprises seeking to reduce DBA overhead, accelerate time-to-market, improve reliability, and adopt modern data architectures such as event-driven, real-time analytics, and multi-model databases.

(Source:https://www.reuters.com/technology/oracle-beats-quarterly-revenue-estimates-2024-09-09)

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

The SQL Server Monitoring Tools market is booming, projected to reach $6.21 Billion by 2033 with a 12% CAGR. Learn about key market drivers, trends, and leading vendors like SolarWinds and Redgate. Discover insights into regional market share and growth opportunities in this comprehensive analysis.

Discover the booming Database Solutions market! This comprehensive analysis reveals a $150 billion market in 2025, projected to grow at a 12% CAGR through 2033. Learn about key trends, drivers, restraints, and regional insights for cloud-based and on-premise solutions. Explore the competitive landscape featuring industry giants like IBM, Amazon, and Microsoft.

The Relational Database Management System (RDBMS) market is booming, projected to reach $126 billion by 2033 with a 12% CAGR. Driven by cloud adoption, data analytics, and digital transformation, key segments include smart government, cybersecurity, and industrial digitalization. Learn more about market trends, leading companies (Oracle, IBM, Amazon), and regional growth projections.

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/