DataOS
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About
DataOS is the world’s first operating system, consisting of a set of primitives, services and modules that are interoperable and composable. These building blocks enable organizations to compose various data architectures and dramatically reduce integrations. Enterprises can have a data-driven decision–making experience equivalent to data-first companies in days and weeks, instead of months and years. It solves many complex data challenges that organizations face by eliminating data siloes, improving scalability, providing advanced security, automating complex data workflows, and making it easy to derive insights from data.
Platform
Task
Features
• observability
• knowledge graph
• data quality
• data sharing
• icebase built-in lakehouse that delivers acid compliance, reliability, and performance for both structured and unstructured data governance automate data access control with granular privacy
• data as a product
• data as software
• data depot
FAQs
What is a “data prison”?
This term describes a situation where data is trapped in silos or systems and cannot be easily shared, analyzed, or integrated with other data sources. A data prison can also result in missed business oportunities.
What is a “data lake”?
A data lake is a centralized repository allowing organizations to store both structured and unstructured data at any scale. Companies can integrate data lakes with other big data tools and technologies.
What is a data lake used for?
Organizations can use data lakes for a variety of purposes, including: Data warehousing; Big data analytics; Machine learning; Data integration; Real-time data processing; Data archiving; Data exploration.
What is a “data fabric”?
A data fabric is a term that describes a seamless and unified data management infrastructure that spans an organization. The data fabric enables data to flow freely and easily between different systems and applications.
What is a “data mesh”?
A data mesh is a decentralized approach to data management that seeks to give data ownership to the business domains or product teams that generate and use the data.
What is the difference between a data fabric and a data mesh?
A data fabric refers to an organization’s centralized and unified data management infrastructure. A data mesh is a decentralized approach to data management that seeks to give data ownership to the business domains.
What is a “data warehouse”?
A data warehouse is a centralized repository for storing and managing large amounts of data from various sources for use in analytics and business intelligence applications.
What is a “data silo”?
A data silo is a data management system or repository that operates independently but has become isolated from other data systems and sources within an organization.
What is “data virtualization”?
Data virtualization is a technology allowing companies to create virtual data layers that abstract and simplify access to data from multiple sources. It provides a unified view of data without physically copying data.
What is a data operating system?
A data operating system is a platform that supports core data functions, including the management, processing, operationalization, and analysis of data across different sources and formats.
What does a data operating system do?
A data operating system provides a connective overlay, uniting all data tools within an organization’s architecture. Users begin building their own reports and queries right away.
How does a data operating system work?
A data operating system provides a centralized data management, integration, and analysis platform. Organizations can unify data from different sources and formats into one platform.
Who needs a data operating system?
Most organizations can benefit from a data operating system since it enables more efficient data management and processes, more accessible and trustworthy insights into data, and data-driven decisions.
Why should data be treated as an asset?
Treating data as an asset means recognizing the value of data as a strategic resource and applying the same principles and practices to data. Every data element and set needs a description, definition, tags, etc.
What are the benefits of treating data as an asset?
Treating data as an asset leads to improvements in data quality, access, collaboration, efficiency, and security. With access to quality data, businesses can improve the identification of business patterns.
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