IBM DataStage es una herramienta de integración de datos para diseñar, desarrollar y ejecutar trabajos que mueven y transforman datos.
DataStage es uno de los componentes de integración de datos de Cloud Pak for Data. El servicio de DataStage está totalmente integrado en Cloud Pak for Data as a Service como parte del entramado de datos. Proporciona una infraestructura gráfica para desarrollar los trabajos que mueven datos desde los sistemas de origen a los sistemas de destino. Los datos transformados se pueden entregar a depósitos de datos, despensas de datos y almacenes de datos operativos, servicios web y sistemas de mensajería en tiempo real, y otras aplicaciones empresariales. DataStage da soporte a los patrones ETL (extraer, transformar y cargar) y ELT (extraer, cargar y transformar). DataStage utiliza el proceso paralelo y la conectividad empresarial para proporcionar una plataforma realmente escalable.
DataStage forma parte de Cloud Pak for Data as a Service y proporciona las prestaciones de integración de datos de la arquitectura de entramado de datos.
Con el tiempo de ejecución remoto como servicio del motor paralelo (PX) de DataStage , puede ejecutar trabajos en IBM Cloud y en ubicaciones remotas precompiladas gestionadas por IBM. Al utilizar una ubicación remota como entorno, puede eliminar total o parcialmente la necesidad de mover o copiar datos de otras nubes públicas. Si incorpora cargas de trabajo a la ubicación de los datos, mejorará el rendimiento, cumplirá los requisitos de residencia de datos e incurrirá en costes de transferencia de datos más bajos.
Con IBM DataStage, su empresa puede alcanzar estos objetivos:
Diseñar flujos de datos que extraen información de varios sistemas de origen, transformar los datos según convenga, y entregar los datos a bases de datos o aplicaciones de destino.
Conectar directamente a aplicaciones empresariales como orígenes o destinos para garantizar que los datos sean relevantes, completos y precisos.
Reducir el tiempo de desarrollo y mejorar la coherencia del diseño y el despliegue, utilizando las funciones creadas previamente.
Minimizar el ciclo de entrega del proyecto trabajando con un conjunto común de herramientas en Watson Studio.
Este servicio añade una herramienta en los proyectos.
Tabla 1. Servicios relacionados. Los siguientes servicios relacionados se utilizan a menudo con este servicio y proporcionan características complementarias, pero no son necesarios.
Cree catálogos de activos organizados con esta plataforma segura de gestión de catálogos de empresa que está soportada por la infraestructura de gobernabilidad de datos.
Prepare, analice y modele datos en un entorno de colaboración con herramientas para científicos de datos, desarrolladores y expertos del dominio.
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Cloud Pak for Data relationship map
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.
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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.