Synthetic Data Generator-AI-powered data generation tool
AI-generated data tailored to your needs
Help I need data, where do I start?
I need to create a mock database for testing.
Can we create synthetic sales data?
How do I generate data for a new project?
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Detailed Introduction to Synthetic Data Generator
The Synthetic Data Generator (SDG) is designed to assist users in creating artificial datasets that mimic real-world data. This tool leverages advanced libraries such as Faker and PyTorch to generate data that is statistically realistic and aligns with specific business or research needs. The core idea behind SDG is to provide high-quality synthetic data for use cases where real data might be unavailable, restricted due to privacy concerns, or insufficient for large-scale simulations. SDG can create custom datasets based on user-defined schemas, maintain relationships between tables (such as foreign keys), and enforce consistency in generated data (e.g., aligning names with gender or generating location-based salary data). For example, SDG can be used to simulate retail transaction data with dependencies like customer demographics, purchase locations, and product information, providing a comprehensive dataset for analysis or machine learning model training. Powered by ChatGPT-4o。
Main Functions of Synthetic Data Generator
Schema-based Data Generation
ExampleIf a user provides a database schema or SQL script defining tables and columns (e.g., a customer table with name, email, gender, etc.), SDG will generate synthetic data that fits this structure.
ScenarioA company testing a new customer relationship management (CRM) software needs realistic but anonymized data. SDG can create a dataset where the generated names align with gender, and email addresses match customer names.
Sample Data Expansion
ExampleIf a user uploads sample data (e.g., CSV files containing a few rows from an existing sales database), SDG will analyze the schema and generate a large dataset that expands the existing structure.
ScenarioA retailer has a small set of transactional data from a pilot store. SDG can scale this sample data into a dataset of 10,000+ transactions for simulations, maintaining relationships between products, customers, and sales.
Starting from Scratch - Custom Data Design
ExampleA user specifies a scenario, such as creating a dataset for an online job board. SDG helps design relevant tables (e.g., job postings, company profiles) and generates data tailored to the industry, with custom attributes like salaries or job descriptions.
ScenarioA startup building a job board needs synthetic data to test their platform. SDG generates realistic job postings with accurate city-based salary distributions, technical job descriptions, and employer details.
Foreign Key Management and Data Consistency
ExampleSDG generates related tables with consistent relationships between them. For example, if a sales dataset contains customer IDs that link to a customer table, the synthetic data will maintain these foreign key relationships.
ScenarioIn a financial system simulation, SDG creates tables for transactions, accounts, and customers, ensuring that each transaction has a valid customer ID and that account balances are consistently generated.
Realistic Statistical Distributions
ExampleUsing PyTorch, SDG can generate numerical data that follows a specified distribution. For instance, user-specified salary ranges can follow a normal distribution with skewed higher salaries for urban areas like London.
ScenarioAn HR analytics company needs data on employee salaries across different regions. SDG can create salary distributions that realistically reflect urban versus rural job markets, making the dataset suitable for model training.
Ideal Users of Synthetic Data Generator
Data Scientists and Analysts
Data scientists need large, representative datasets to train machine learning models, test algorithms, and analyze trends. SDG provides synthetic datasets when real-world data is not available or needs to be anonymized. By generating realistic data, SDG allows data scientists to develop and evaluate models in a controlled environment, ensuring that the data is diverse and statistically sound.
Software Developers and QA Teams
Developers and quality assurance teams benefit from SDG by using it to test software systems under realistic data loads. Whether it's a new CRM, financial system, or retail application, SDG generates synthetic data that mirrors real-world scenarios, enabling developers to identify potential issues and QA teams to simulate various edge cases.
Academic Researchers
Researchers often need data for experiments, simulations, and hypothesis testing. In fields like economics, healthcare, and social sciences, where privacy concerns limit access to sensitive datasets, SDG allows researchers to generate datasets that replicate real-world characteristics while maintaining confidentiality.
Business Intelligence and Reporting Teams
BI teams require data to create dashboards and reports for decision-making. When real data is unavailable or incomplete, SDG provides datasets that reflect the business environment (e.g., sales data, customer demographics) so that BI teams can generate meaningful insights and prototypes for stakeholders.
Startups and Entrepreneurs
Startups often need to demonstrate their software or platforms using realistic data. SDG helps them create datasets that reflect the needs of their target audience (e.g., a new e-commerce platform can showcase data like customer orders and inventory), allowing them to validate their ideas and pitch to investors or customers.
How to Use the Synthetic Data Generator
Step 1
Visit yeschat.ai for a free trial without login; no need for ChatGPT Plus. Begin by exploring the tool's capabilities immediately.
Step 2
Define the context or scenario for data generation. Upload sample data, provide a schema, or start from scratch depending on your project needs.
Step 3
Plan the data generation process. Specify row counts, table relationships, and field-specific rules, such as gender balance, foreign keys, or realistic location details.
Step 4
Generate data step-by-step, adjusting parameters as needed. Review initial outputs and refine any data columns or structures that need tuning.
Step 5
Export your final data and Python code as Jupyter notebooks. Create realistic datasets with foreign key relationships, business-specific requirements, and more.
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Frequently Asked Questions About Synthetic Data Generator
What type of scenarios can I use Synthetic Data Generator for?
Synthetic Data Generator can be used for a wide range of scenarios, such as testing machine learning models, generating datasets for academic research, or creating sample data for business simulations. It’s flexible enough to handle transactional data, customer profiles, and more.
How does it ensure data realism in generated datasets?
It uses AI techniques and libraries like Faker for general data generation and PyTorch for producing statistically realistic attributes. The generator aligns data based on user-defined rules, such as consistent names and emails, foreign keys, and weighted distribution of attributes like gender and location.
Can I generate linked datasets with foreign key relationships?
Yes, the generator ensures data consistency across multiple tables by creating proper foreign key relationships. This feature is particularly useful for generating complex datasets with realistic relationships, such as sales data linked to customer profiles.
What are the limits on dataset size?
While you can generate datasets up to around 100,000 rows in the sandbox environment, for larger datasets you can export the code and run it on a larger cluster for scalability.
Can I export the generated data?
Yes, after generating the datasets, you can export them in multiple formats such as CSV, Parquet, or even as a Jupyter Notebook containing the Python code that was used to generate the data.