YData Fabric SDK

🚀 YData Fabric SDK 🎉 Fabric's platform capabilities at the distance of a Python command!

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YData Fabric SDK empowers developers with easy access to state-of-the-art data quality tools and generative AI capabilities. Stay tuned for more updates and new features!

Documentation | More on YData

Overview

The Fabric SDK is an ecosystem of methods that allows users to, through a python interface, adopt a Data-Centric approach towards the AI development. The solution includes a set of integrated components for data ingestion, standardized data quality evaluation and data improvement, such as synthetic data generation, allowing an iterative improvement of the datasets used in high-impact business applications.

Synthetic data can be used as Machine Learning performance enhancer, to augment or mitigate the presence of bias in real data. Furthermore, it can be used as a Privacy Enhancing Technology, to enable data-sharing initiatives or even to fuel testing environments.

Under the Fabric SDK hood, you can find a set of algorithms and metrics based on statistics and deep learning based techniques, that will help you to accelerate your data preparation.

What you can expect:

Fabric SDK is composed by the following main modules:

• Datasources

  • Fabric’s SDK includes several connectors for easy integration with existing data sources. It supports several storage types, like filesystems and RDBMS. Check the list of connectors.
  • Fabric SDK’s Datasources run on top of Dask, which allows it to deal with not only small workloads but also larger volumes of data.

• Synthesizers

  • Simplified interface to train a generative model and learn in a data-driven manner the behavior, the patterns and original data distribution. Optimize your model for privacy or utility use-cases.
  • From a trained synthesizer, you can generate synthetic samples as needed and parametrise the number of records needed.

• Synthetic data quality report Coming soon

  • An extensive synthetic data quality report that measures 3 dimensions: privacy, utility and fidelity of the generated data. The report can be downloaded in PDF format for ease of sharing and compliance purposes or as a JSON to enable the integration in data flows.

• Profiling Coming soon

  • A set of metrics and algorithms summarizes datasets quality in three main dimensions: warnings, univariate analysis and a multivariate perspective.

Supported data formats

• Tabular The RegularSynthesizer is perfect to synthesize high-dimensional data, that is time-independent with high quality results.
• Time-Series The TimeSeriesSynthesizer is perfect to synthesize both regularly and not evenly spaced time-series, from smart-sensors to stock.