IBM DataStage è uno strumento di integrazione dati per la progettazione, lo sviluppo e l'esecuzione di job che spostano e trasformano i dati.
DataStage è uno dei componenti di integrazione dati di Cloud Pak for Data. Il servizio DataStage è completamente integrato in Cloud Pak for Data as a Service come parte del data fabric. Fornisce un framework grafico per sviluppare i job che spostano i dati dai sistemi di origine ai sistemi di destinazione. I dati trasformati possono essere forniti a data warehouse, data mart e archivi di dati operativi, servizi web e sistemi di messaggistica in tempo reale e altre applicazioni enterprise. DataStage supporta i pattern extract, transform, load (ETL) ed extract, load e transform (ELT). DataStage utilizza l'elaborazione parallela e la connettività aziendale per fornire una piattaforma realmente scalabile.
DataStage fa parte di Cloud Pak for Data as a Service e fornisce le funzionalità di integrazione dei dati dell'architettura del data fabric.
Con DataStage parallel engine (PX) remote runtime as - a - service, puoi eseguire i job in IBM Cloud e in ubicazioni remote preintegrate gestite da IBM. Utilizzando un'ubicazione remota come ambiente, è possibile eliminare completamente o parzialmente la necessità di spostare o copiare i dati da altri cloud pubblici. Portando i carichi di lavoro nell'ubicazione dei dati, si migliorano le prestazioni, si soddisfano i requisiti di residenza dei dati e si riducono i costi di trasferimento dei dati.
Con DataStage, la vostra azienda può raggiungere questi obiettivi:
Progetta flussi di dati che estraggono informazioni da più sistemi di origine, trasformano i dati come richiesto e forniscono i dati ai database o alle applicazioni di destinazione.
Collegarsi direttamente alle applicazioni enterprise come origini o destinazioni per garantire che i dati siano pertinenti, completi e precisi.
Riduci i tempi di sviluppo e migliora la coerenza di progettazione e implementazione utilizzando funzioni preintegrate.
ridurre al minimo il ciclo di distribuzione del progetto utilizzando una serie comune di strumenti in Watson Studio.
Questo servizio aggiunge uno strumento nei progetti.
Tabella 1. Servizi correlati. I seguenti servizi correlati sono spesso utilizzati con questo servizio e forniscono funzioni complementari, ma non sono richiesti.
Prepara, analizza e modella i dati in un ambiente collaborativo con strumenti per data scientist, sviluppatori ed esperti di 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.
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.