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.
Tasks you'll do
Some tasks have a choice of tools and services.
Tools you'll use
Some tools perform the same tasks but have different features and levels of automation.
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
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.
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Automate the model lifecycle, including preparing data, training models, and creating deployments.
Work with R notebooks and scripts in an integrated development environment.
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deploy and run your data science and AI solutions in a test or production environment.
Find and share your data and other assets.
Import asset metadata from a connection into a project or a catalog.
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Measure and monitor the quality of your data.
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Create your business vocabulary to enrich assets and rules to protect data.
Track data movement and usage for transparency and determining data accuracy.
Track AI models from request to production.
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Create a virtual table to segment or combine data from one or more tables.
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
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.
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
Discover, profile, catalog, and share trusted data in your organization.
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
View, access, manipulate, and analyze your data without moving it.
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
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.
Where you work with data.
> Projects > View all projects
Where you find and share assets.
> Catalogs > View all catalogs
Where you deploy and run assets that are ready for testing or production.
> Deployments
Where you manage governance artifacts.
> Governance > Categories
Where you virtualize data.
> Data > Data virtualization
Where you consolidate data into a 360 degree view.
> Data > Master data
Where you track and govern models.
> Catalogs > Model inventory
Where you monitor and evaluate models.
> Deployments
Where you view data lineage.
> Data > Data lineage
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Focus sentinel
Data Virtualization
Description
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Data Virtualization enables access to physical data from various sources in a
virtual manner, so that the data can be accessed, manipulated, and analyzed from one central
location, without the need to know its physical format or location, and without having to move or
copy it.
Data Virtualization is fully integrated into Cloud Pak for Data as a Service on IBM Cloud as part of the data fabric. Data Virtualization provides the virtualization
capabilities of the data fabric architecture.
To get started, create a service instance of Data Virtualization and launch it in Cloud Pak for Data as a Service. Then, create connections to your data sources so that you can
quickly create views across all of your organization’s data.
With Data Virtualization, your company can accomplish these goals:
Simplify your analytics and make them more accurate because you’re querying the latest data at
its source.
Use real-time analytics efficiently and get current analytics for distributed data sources, with
no need to store data outside your data center.
Accelerate processing times by automatically organizing your data nodes into a collaborative
network for computational efficiency.
Take advantage of standard SQL through common interfaces such as R, Spark, Python, and Jupyter
Notebooks in a single data repository where your SQL applications can connect and run.
Centralize authentication and authorization for data sources in a trusted environment where
credentials for your private databases are stored encrypted at the local device and are private to
that device.
This service adds a workspace to Cloud Pak for Data as a Service.
Use cases
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The following table describes how Data Virtualization addresses critical needs of an organization:
Problem statement
What Data Virtualization
enables
Value
Making use of a lot of data across different locations and formats is challenging and leads
to a complex data pipeline.
A semantic layer that sits on top of the data sprawl that enables users to query across
different data sources and formats in real time.
Empower data consumers to self-service.
Storing data across different cloud and on-premises locations with software and systems that do
not work together seamlessly to create end-to-end data pipelines.
Data engineers can quickly fulfill ad hoc data integration requests to validate hypothesis or
“what-if” scenarios with security and governance.
Accelerate the data lifecycle and reduce time to value for addressing business
questions.
Inability to manage governance and enforce privacy regulations at scale.
Abstract data governance and enforce data policies across all your data sources through a
single layer.
Increase compliance with data protection regulations while reducing overhead of managing
access control at scale.
Create catalogs of curated assets with this secure enterprise catalog management platform
that is supported by a data governance framework.
Integrated services
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Table 2. Related services. The following related services are often used with this service and
provide complementary features, but they are not required.