Best Synthetic Data Generation Tools for Microsoft Azure
View:
Compare the Top Synthetic Data Generation Tools that integrate with Microsoft Azure as of June 2025
Sort By:
This a list of Synthetic Data Generation tools that integrate with Microsoft Azure. Use the filters on the left to add additional filters for products that have integrations with Microsoft Azure. View the products that work with Microsoft Azure in the table below.
What are Synthetic Data Generation Tools for Microsoft Azure?
Synthetic data generation tools are software programs used to produce artificial datasets for a variety of purposes. They use a range of algorithms and techniques to create data that is statistically similar to existing real-world data but does not contain any personal identifiable information. These tools can help organizations test their products and systems in various scenarios without compromising user privacy. The generated synthetic data can also be used for training machine learning models as an alternative to using real-life datasets. Compare and read user reviews of the best Synthetic Data Generation tools for Microsoft Azure currently available using the table below. This list is updated regularly.
-
1
YData
YData
Adopting data-centric AI has never been easier with automated data quality profiling and synthetic data generation. We help data scientists to unlock data's full potential. YData Fabric empowers users to easily understand and manage data assets, synthetic data for fast data access, and pipelines for iterative and scalable flows. Better data, and more reliable models delivered at scale. Automate data profiling for simple and fast exploratory data analysis. Upload and connect to your datasets through an easily configurable interface. Generate synthetic data that mimics the statistical properties and behavior of the real data. Protect your sensitive data, augment your datasets, and improve the efficiency of your models by replacing real data or enriching it with synthetic data. Refine and improve processes with pipelines, consume the data, clean it, transform your data, and work its quality to boost machine learning models' performance. -
2
Protecto
Protecto
While enterprise data is exploding and scattered across various systems, oversight of driving privacy, data security, and governance has become very challenging. As a result, businesses hold significant risks in the form of data breaches, privacy lawsuits, and penalties. Finding data privacy risks in an enterprise is a complex, and time-consuming effort that takes months involving a team of data engineers. Data breaches and privacy laws are requiring companies to have a better grip on which users have access to the data, and how the data is used. But enterprise data is complex, so even if a team of engineers works for months, they will have a tough time isolating data privacy risks or quickly finding ways to reduce them.Starting Price: Usage based -
3
DATPROF
DATPROF
Test Data Management solutions like data masking, synthetic data generation, data subsetting, data discovery, database virtualization, data automation are our core business. We see and understand the struggles of software development teams with test data. Personally Identifiable Information? Too large environments? Long waiting times for a test data refresh? We envision to solve these issues: - Obfuscating, generating or masking databases and flat files; - Extracting or filtering specific data content with data subsetting; - Discovering, profiling and analysing solutions for understanding your test data, - Automating, integrating and orchestrating test data provisioning into your CI/CD pipelines and - Cloning, snapshotting and timetraveling throug your test data with database virtualization. We improve and innovate our test data software with the latest technologies every single day to support medium to large size organizations in their Test Data Management. -
4
AutonomIQ
AutonomIQ
Our AI-driven, autonomous low-code automation platform is designed to help you achieve the highest quality outcome in the shortest amount of time possible. Generate automation scripts automatically in plain English with our Natural Language Processing (NLP) powered solution, and allow your coders to focus on innovation. Maintain quality throughout your application lifecycle with our autonomous discovery and up-to-date tracking of changes. Reduce risk in your dynamic development environment with our autonomous healing capability and deliver flawless updates by keeping automation current. Ensure compliance with all regulatory requirements and eliminate security risk using AI-generated synthetic data for all your automation needs. Run multiple tests in parallel, determine test frequency, keep pace with browser updates and executions across operating systems and platforms. -
5
MOSTLY AI
MOSTLY AI
As physical customer interactions shift into digital, we can no longer rely on real-life conversations. Customers express their intents, share their needs through data. Understanding customers and testing our assumptions about them also happens through data. And privacy regulations such as GDPR and CCPA make a deep understanding even harder. The MOSTLY AI synthetic data platform bridges this ever-growing gap in customer understanding. A reliable, high-quality synthetic data generator can serve businesses in various use cases. Providing privacy-safe data alternatives is just the beginning of the story. In terms of versatility, MOSTLY AI's synthetic data platform goes further than any other synthetic data generator. MOSTLY AI's versatility and use case flexibility make it a must-have AI tool and a game-changing solution for software development and testing. From AI training to explainability, bias mitigation and governance to realistic test data with subsetting, referential integrity. -
6
GenRocket
GenRocket
Enterprise synthetic test data solutions. In order to generate test data that accurately reflects the structure of your application or database, it must be easy to model and maintain each test data project as changes to the data model occur throughout the lifecycle of the application. Maintain referential integrity of parent/child/sibling relationships across the data domains within an application database or across multiple databases used by multiple applications. Ensure the consistency and integrity of synthetic data attributes across applications, data sources and targets. For example, a customer name must always match the same customer ID across multiple transactions simulated by real-time synthetic data generation. Customers want to quickly and accurately create their data model as a test data project. GenRocket offers 10 methods for data model setup. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce. -
7
Subsalt
Subsalt Inc.
Subsalt is the first platform built to enable the use of anonymous data at enterprise scale. Subsalt's Query Engine dynamically optimizes the tradeoffs between data privacy and fidelity to the source data. Queries return fully-synthetic data that preserves row-level granularity and data formats without disruptive data transformations. Subsalt provides compliance guarantees supported by third-party audits that satisfy HIPAA's Expert Determination standard. Subsalt supports multiple deployment models to meet the unique privacy and security requirements of each client. Subsalt is SOC2-Type 2 and HIPAA compliant. The system has been designed to minimize the risk of exposure or breach of real data. Existing data and ML tools integrate directly with Subsalt's Postgres-compatible SQL interface, making adoption a breeze. -
8
Syntho
Syntho
Syntho typically deploys in the safe environment of our customers so that (sensitive) data never leaves the safe and trusted environment of the customer. Connect to the source data and target environment with our out-of-the-box connectors. Syntho can connect with every leading database & filesystem and supports 20+ database connectors and 5+ filesystem connectors. Define the type of synthetization you would like to run, realistically mask or synthesize new values, automatically detect sensitive data types. Utilize and share the protected data securely, ensuring compliance and privacy are maintained throughout its usage.
- Previous
- You're on page 1
- Next