Stratio Data Fabric

Automatically unifies data from all diverse sources, whether on-premises or across various cloud environments, providing a single, consistent view.

Leverages AI to automatically improve data quality, bolster security, and enrich the business context of your data, reducing manual effort and errors.

Designed for flexibility, allowing organizations to deploy only the specific components they need and seamlessly integrate with their existing tech stack, saving time and resources.

Automates a significant portion (up to 80%) of routine data management tasks, including discovery, cataloging, integration, and governance workflows.

Enables robust and active data governance through features like automatic mapping with technical data and a semantic layer, ensuring data is managed effectively.

Provides fine-grained security, allowing precise control over who can access specific data assets and systems, enhancing data protection.

Speeds up the creation of new data products by providing a unified, governed, and easily accessible data foundation.

Facilitates quicker development and deployment of machine learning models by providing streamlined access to quality data.

Promotes the rapid integration and utilization of generative AI capabilities within the enterprise.

Accesses data directly at its source through virtualization, eliminating the need to move or replicate data, which improves efficiency and reduces data duplication.

Automatically creates a business-friendly semantic layer, defining business terms and mapping them to underlying technical data for easier understanding and use.

Allows users to interact with their data using natural language, enabling them to "talk to their data" and receive answers in various formats like text, tables, or dashboards.

Utilizes Stratio's AI models to automate various tasks, from transforming insights into action to automating customer service interactions and optimizing operational levels.

Automatically compiles a comprehensive and intelligent data catalog that identifies relationships between datasets, benefiting all users with AI-driven decision-making.

Built on Kubernetes, allowing for horizontal and vertical auto-scaling, which ensures optimal performance and resource utilization.

Features a distributed architecture that imposes no limits on data volume or processing speed, ensuring high scalability for growing needs.

Offers the flexibility to deploy the solution anywhere, including any public cloud, private cloud, or on-premises environment.

Designed with a focus on usability, ensuring that users can easily interact with and leverage the advanced technology.

Employs transparent pay-per-use and pay-per-core pricing models, making costs simple to understand and manage.

Helps manage statutory, regulatory, and financial reporting while ensuring strict data privacy compliance with standards like GDPR, PII, and PCI.

Supports the rapid migration of existing data lakes and other data assets into public cloud environments.

Provides a reliable and governed foundation for building reporting, virtualization, ETLs (Extract, Transform, Load), data classification, predictions, MLOps, and Test Data Management (TDM).

Combines data virtualization and data ingestion in a single architecture to cover all data scenarios, including batch, near real-time (NRT), informational, and operational use cases for both structured and unstructured data.

Enables organizations to decouple and reduce the number of legacy systems while ensuring critical business applications remain functional.

Supports various generative AI applications such as creating chatbots, Q&A systems, summarizing text, and generating expert recommendations from data.

Facilitates secure access to highly accurate data through natural language, significantly reducing the time required to process complex data requests (from weeks to minutes).

Helps remove data silos and effortlessly combines structured and unstructured datasets from all storage locations to provide richer and more comprehensive responses to user queries.