Unity Catalog
Databricks Unity Catalog is the industry’s only unified and open governance solution for data and AI, built into the Databricks Data Intelligence Platform. With Unity Catalog, organizations can seamlessly govern both structured and unstructured data in any format, as well as machine learning models, notebooks, dashboards, and files across any cloud or platform. Data scientists, analysts, and engineers can securely discover, access, and collaborate on trusted data and AI assets across platforms, leveraging AI to boost productivity and unlock the full potential of the lakehouse environment. This unified and open approach to governance promotes interoperability and accelerates data and AI initiatives while simplifying regulatory compliance. Easily discover and classify both structured and unstructured data in any format, including machine learning models, notebooks, dashboards, and files across all cloud platforms.
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DataChain
DataChain connects unstructured data in cloud storage with AI models and APIs, enabling instant data insights by leveraging foundational models and API calls to quickly understand your unstructured files in storage. Its Pythonic stack accelerates development tenfold by switching to Python-based data wrangling without SQL data islands. DataChain ensures dataset versioning, guaranteeing traceability and full reproducibility for every dataset to streamline team collaboration and ensure data integrity. It allows you to analyze your data where it lives, keeping raw data in storage (S3, GCP, Azure, or local) while storing metadata in inefficient data warehouses. DataChain offers tools and integrations that are cloud-agnostic for both storage and computing. With DataChain, you can query your unstructured multi-modal data, apply intelligent AI filters to curate data for training and snapshot your unstructured data, the code for data selection, and any stored or computed metadata.
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Visual Layer
Visual Layer is a platform for working with large volumes of image and video data. It supports visual search, filtering, tagging, and dataset structuring across raw files, metadata, and labels. No code is required, and both technical and non-technical teams use it in production. Common applications include curating datasets for machine learning, auditing visual content for compliance, reviewing surveillance material, and preparing media for downstream platforms.
The platform detects duplicates, mislabeled items, outliers, and low-quality files to improve data quality before model training or operational decision-making. It is model-agnostic, supports both cloud and on-premise deployment, and is built by the creators of Fastdup, the widely used open-source tool for visual deduplication.
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Wolfram Data Science Platform
Wolfram Data Science Platform lets you use data sources that are structured or unstructured, and static or real-time. Use the power of WDF and the same linguistics as in Wolfram|Alpha to convert unstructured data to structured form, with automated or guided destructuring and disambiguation. Wolfram Data Science Platform uses industry database connection technology to bring database content into its highly flexible internal symbolic representation. Wolfram Data Science Platform can natively read hundreds of data formats, converting them. Wolfram Data Science Platform works with images, text, networks, geometry, sounds, GIS data and much more. Using the breakthrough symbolic data representation in the Wolfram Language, Wolfram Data Science Platform can seamlessly handle both SQL-style and NoSQL data. Wolfram Data Science Platform automatically constructs a sophisticated interactive report, using algorithms to identify interesting features of your data to visualize and highlight.
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