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Virtualización de datos
Última actualización: 21 mar 2025
Virtualización de datos con Data Virtualization
Utilice el Data Virtualization para unir fácilmente datos de diferentes fuentes en una vista unificada, sin cambios manuales, movimiento de datos o replicación.
El servicio de Data Virtualization forma parte del tejido de datos.
Visión general
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Con Data Virtualization, puede acceder a datos físicos de múltiples fuentes a través de una única capa virtual semántica. Esta capa virtual significa que se puede acceder a los datos, manipularlos y analizarlos sin necesidad de conocer su formato físico o ubicación, y sin tener que moverlos o copiarlos.
Data Virtualization forma parte del tejido de datos.
Requisitos previos
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Si desea publicar sus datos virtuales en un catálogo regulado, debe instalar IBM
Knowledge Catalog. Para más información, consulte IBM Knowledge Catalog en Cloud Pak for Data.
Data Virtualization utiliza sus credenciales " IBM
Cloud " para conectarse al servicio. Debe tener ciertos roles de Data Virtualization para realizar ciertas tareas. Para obtener más información, consulte Conexión y autenticación en el servicio Data Virtualization.
Cómo empezar
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Para empezar a utilizar Data Virtualization, siga estos pasos generales:
Abrir el Data Virtualization servicio.
En el menú de navegación de Cloud Pak for Data , seleccione Data > Data virtualization.
Añada sus fuentes de datos a Data Virtualization.
Vaya a la página Fuentes de datos y seleccione Añadir conexión para añadir conexiones. Data Virtualization admite docenas de fuentes de datos relacionales y no relacionales.
Virtualizar las tablas desde la fuente de datos.
En la página Virtualizar, seleccione las mesas que desea virtualizar y, a continuación, seleccione Añadir al carrito > Ver carrito para virtualizar las mesas.
Únase a las tablas para crear una vista unificada.
En la página Datos virtualizados, seleccione las tablas que desea unir y, a continuación, seleccione Unir para unir los objetos.
Consulta los objetos virtuales.
Navegue hasta la página Ejecutar SQL para consultar sus objetos virtuales utilizando el editor SQL integrado.
Consumir los datos utilizando otros servicios de Cloud Pak for Data en la estructura de datos.
Consume tablas virtuales en proyectos, paneles de control, catálogos de datos y otras aplicaciones. Para más información, consulte Servicios de panel de control.
Vea el siguiente vídeo para obtener una visión general de Data Virtualization.
En este vídeo se proporciona un método visual como alternativa a la documentación escrita.
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Some tools perform the same tasks but have different features and levels of automation.
Jupyter notebook editor
Prepare data
Visualize data
Build models
Deploy assets
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
AutoAI
Build models
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
SPSS Modeler
Prepare data
Visualize data
Build models
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Decision Optimization
Build models
Visualize data
Deploy assets
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Data Refinery
Prepare data
Visualize data
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Orchestration Pipelines
Prepare data
Build models
Deploy assets
Automate the model lifecycle, including preparing data, training models, and creating deployments.
RStudio
Prepare data
Build models
Deploy assets
Work with R notebooks and scripts in an integrated development environment.
Federated learning
Build models
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deployments
Deploy assets
Monitor models
Deploy and run your data science and AI solutions in a test or production environment.
Catalogs
Catalog data
Governance
Find and share your data and other assets.
Metadata import
Prepare data
Catalog data
Governance
Import asset metadata from a connection into a project or a catalog.
Metadata enrichment
Prepare data
Catalog data
Governance
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Data quality rules
Prepare data
Governance
Measure and monitor the quality of your data.
Masking flow
Prepare data
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Governance
Governance
Create your business vocabulary to enrich assets and rules to protect data.
Data lineage
Governance
Track data movement and usage for transparency and determining data accuracy.
AI factsheet
Governance
Monitor models
Track AI models from request to production.
DataStage flow
Prepare data
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Data virtualization
Prepare data
Create a virtual table to segment or combine data from one or more tables.
OpenScale
Monitor models
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Data replication
Prepare data
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Master data
Prepare data
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
watsonx.ai Studio
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
watsonx.ai Runtime
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
IBM Knowledge Catalog
Discover, profile, catalog, and share trusted data in your organization.
DataStage
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
Data Virtualization
View, access, manipulate, and analyze your data without moving it.
Watson OpenScale
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Data Replication
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Match360 with Watson
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Manta Data Lineage
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Project
Where you work with data.
> Projects > View all projects
Catalog
Where you find and share assets.
> Catalogs > View all catalogs
Space
Where you deploy and run assets that are ready for testing or production.
> Deployments
Categories
Where you manage governance artifacts.
> Governance > Categories
Data virtualization
Where you virtualize data.
> Data > Data virtualization
Master data
Where you consolidate data into a 360 degree view.
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