Fabric

The data development platform for structured data

The fastest path to deliver AI solutions.
Automated data profiling and synthetic data in a workbench that unlocks production-quality data.
Data preparation matters

The Data-Centric workflow

YData's mission is to accelerate the AI development through improved data

Fabric provides automated data profiling, augmentation, cleaning and selection, in a continuous flow to improve training data and models performance.

Graph about data-centric workflow
How it works

Understand, Explore, Enrich, Scale

Set projects and access a collaborative environment in a few minutes. Connect, manage & understand your data assets in a few clicks.

Start improving the quality of your data and you Machine Learning models performance at scale within days not months.

Ingest data from FileSystems to RDBMS' in a few steps
Understand your data assets with automated data profiling
Generate synthetic data in a few clicks
Experiment in a familiar environment with Jupyter Labs and VS Code
Build, version & iterate your data preparation flows with pipelines
dashboard
data catalog fabric
Understand

Data Catalog

Simplified, scalable and simple connection to a variety of data sources. Understand your data assets through automated profiling and detection of quality issues for faster exploratory data analysis and data preparation.



Explore

Labs

On-demand development environments with configurable hardware (including GPUs). Support for Python & R for a no-learning curve data experimentation space.

Supercharged with the most popular DS libraries and the YData SDK.

jupyter_lab_logo
visual studio code
h2o ai logo
synth data
Enrich

Synthetic data

Artificially generated data that doesn’t match any individual record. While resembling real data, synthetic data ensures both business value while being compliant with privacy regulations.

Synthetic data is great to enable data-sharing initiatives or to boost ML models performance.

Scale

Pipelines

General-purpose job orchestrator with built-in scalability, modularity for experiment tracking capabilities. Pipelines bring the Ops to your Data-Centric AI workflows.

pipelines

How do we do it?

From raw to smart data in a few steps

  • Profile, process and improve the quality of your data with a seamless experience through our UI interface or with code in an IDE of your preference.

Deploy Fabric in a cloud of your choice

Get started today

Become the best in class by delivering faster and better AI solutions with improved data.

How to pick the best fit data catalog for your data stack?

Dive into data management with our latest whitepaper, which presents an in-depth Gap analysis among YData Fabric, Alation, and Informatica—three solutions in the realm of data catalogs. These platforms are chaging how organizations govern,...

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How to evaluate the re-identification risk in Synthetic Data?

While allowing for meaningful data behavior, it is crucial that synthetic data safeguards individual privacy. Therefore, ensuring the efficacy of synthetic data applications also requires a strong assessment of re-identification risks.

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How is diversity preserved while ensuring privacy in synthetic data?

One of the most valuable and unique characteristics of synthetic data is that it keeps the properties and behavior of original data without a one-to-one link with the real events, thus fostering data privacy and enabling secure data...

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