This dataset consists of 8,034 entries designed to evaluate the performance of text-to-SQL models. Each entry contains a natural language text query and its corresponding SQL command. The dataset is a subset derived from the Spider dataset, focusing on diverse and complex queries to challenge the understanding and generation capabilities of machine learning models.

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

This dataset is designed for training models to convert natural language prompts into SQL queries, specifically focusing on SELECT statements. The dataset comprises 14,815 examples where each prompt is associated with the corresponding SQL query that would retrieve the desired information from a specific table.

Columns: Prompt: The natural language text representing a query request. SQL Query: The corresponding SQL query generated to fulfill the request.

This dataset contains information about housing sales in Nashville, TN such as property, owner, sales, and tax information. The SQL queries I created for Data Cleaning can be found here.

Context

I've always wanted to explore Kaggle's Meta Kaggle dataset but I am more comfortable on using TSQL when it comes to writing (very) complex queries. Also, I tend to write queries faster when using SQL MANAGEMENT STUDIO, like 100x faster. So, I ported Kaggle's Meta Kaggle dataset into MS SQL SERVER 2022 database format, created a backup file, then uploaded it here.

• MSSQL VERSION: SQL Server 2022
• Collation: SQL_Latin1_General_CP1_CI_AS
• Recovery model: simple

Requirements

• Download and install the SQL SERVER 2022 Developer edition here
• Download the backup file
• Restore the backup file into your local. If you havent done this before, it's easy and straightforward. Here is a guide.

(QUOTED FROM THE ORIGINAL DATASET)

Meta Kaggle

Explore Kaggle's public data on competitions, datasets, kernels (code/ notebooks) and more Meta Kaggle may not be the Rosetta Stone of data science, but they think there's a lot to learn (and plenty of fun to be had) from this collection of rich data about Kaggle’s community and activity.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F2ad97bce7839d6e57674e7a82981ed23%2F2Egeb8R.png?generation=1688912953875842&alt=media" alt="">

Notes

• I repeat, I just ported the dataset. All credits to Kaggle for the amazing source dataset.
• Cover image from https://picryl.com/media/space-earth-bug-ce3ca6

SQL Injection Dataset:

A cleaned SQL injection dataset, sourced from multiple Kaggle datasets, has been cleaned and split into training, validation, and testing subsets with a 6:2:2 ratio. This dataset is intended for use in research focused on detecting SQL injection attacks.

Kaggle datasets:

• https://www.kaggle.com/datasets/syedsaqlainhussain/sql-injection-dataset/data
• https://www.kaggle.com/datasets/sajid576/sql-injection-dataset
• https://www.kaggle.com/datasets/gambleryu/biggest-sql-injection-dataset
• https://www.kaggle.com/datasets/mahdighaemi/sql-injection
• https://www.kaggle.com/datasets/kholoodsalah/sql-injection-dataset
• https://www.kaggle.com/datasets/alextrinity/sqlinjectionextend

The dataset contains two columns:

1. Query: This column represents the SQL query.

2. Label: This column represents the label for the SQL injection binary classification, where 1 indicates an SQL injection and 0 indicates a non-SQL injection.

   https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20832724%2Fc0d648e947cec02e35beb665b24b5bdb%2Fsql-injection-datasets.png?generation=1718710914956418&alt=media" alt="">

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.

I imported the two Olist Kaggle datasets into an SQLite database. I modified the original table names to make them shorter and easier to understand. Here's the Entity-Relationship Diagram of the resulting SQLite database:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2473556%2F23a7d4d8cd99e36e32e57303eb804fff%2Fdb-schema.png?generation=1714391550829633&alt=media" alt="Database Schema">

Data sources:

https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce

https://www.kaggle.com/datasets/olistbr/marketing-funnel-olist

I used this database as a data source for my notebook:

SQL Challenge: E-commerce data analysis

This dataset is a practical SQL case study designed for learners who are looking to enhance their SQL skills in analyzing sales, products, and marketing data. It contains several SQL queries related to a simulated business database for product sales, marketing expenses, and location data. The database consists of three main tables: Fact, Product, and Location.

Objective of the Case Study: The purpose of this case study is to provide learners with a variety of practical SQL exercises that involve real-world business problems. The queries explore topics such as:

• Aggregating data (e.g., sum, count, average)
• Filtering and sorting data
• Grouping and joining multiple tables
• Using SQL functions like AVG(), COUNT(), SUM(), and MIN/MAX()
• Handling advanced SQL features such as row numbering, transactions, and stored procedures

SQL IMDB Movies Analysis for RSVP (Film Production Company)

RSVP Movies is an Indian film production company which has produced many super-hit movies. They have usually released movies for the Indian audience but for their next project, they are planning to release a movie for the global audience in 2022.

The production company wants to plan their every move analytically based on data. We have taken the last three years IMDB movies data and carried out the analysis using SQL. We have analysed the data set and drew meaningful insights that could help them start their new project.

For our convenience, the entire analytics process has been divided into four segments, where each segment leads to significant insights from different combinations of tables. The questions in each segment with business objectives are written in the script given below. We have written the solution code below every question.

Famous paintings and their artists. This data set is published to help students have interesting data to practice SQL

Original Data

Acknowlegement

Foto von Steve Johnson auf Unsplash

Dataset

This dataset was created by Rodrigo Fontanella

Contents

Dataset

This dataset was created by Andrew Dolcimascolo-Garrett

Released under MIT

Contents

Dataset

This dataset was created by Michael Nowell

Released under Database: Open Database, Contents: Database Contents

Contents

Dataset

This dataset was created by Yentür

Released under MIT

Contents

Description

🔍 Overview

This dataset is built for Text-to-SQL (NL → SQL) tasks, helping train models to convert natural language into SQL queries. It is ideal for fine-tuning LLMs, developing AI-powered database assistants, and improving SQL query generation accuracy.

📂 Dataset Structure

Each row contains the following fields:
- 📝 Instruction – A natural language query (e.g., "Find all customers who placed an order in the last 30 days.")
- 📊 Query – The corresponding SQL statement (e.g., SELECT * FROM orders WHERE order_date >= DATE_SUB(NOW(), INTERVAL 30 DAY);)
- 🗄️ Database – Contains metadata such as:
- Table Names – The relevant tables for the query (e.g., orders, customers)
- Column Names – The specific fields used in the query (e.g., order_date, customer_id)

🚀 Use Cases

• Fine-tuning Large Language Models (LLMs) for SQL generation
• Training AI chatbots to assist with SQL query building
• Developing database assistants for automated SQL generation
• Enhancing Retrieval-Augmented Generation (RAG) for SQL-based applications

Dataset

This dataset was created by Ada Luo daa

Released under Apache 2.0

Contents

Dataset

This dataset was created by vishnusan

Released under Apache 2.0

Contents

Dataset

This dataset was created by Parth Mistry 20

Contents

Dataset

This dataset was created by rayten

Released under Apache 2.0

Contents