IBM Knowledge Catalog on Cloud Pak for Data as a Service
Last updated: Dec 13, 2024
IBM Knowledge Catalog on Cloud Pak for Data as a Service
Description
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IBM Knowledge Catalog, a core service of Cloud Pak for Data as a Service, connects people to the data and knowledge
that they need. The platform is supported by a data governance framework to ensure that data access
and data quality are compliant with your business rules and standards. IBM Knowledge Catalog delivers automated enrichment of data assets with business metadata to
align company policies and vocabularies to data in support of AI, analytics, and compliance use
cases.
IBM Knowledge Catalog provides the data governance and privacy capabilities of the
data fabric architecture.
You develop a knowledge core by curating data assets and enriching them with governance artifacts
that describe their properties and meaning. Data stewards and data engineers curate data by
importing metadata, preparing the data assets, enriching the data assets by assigning governance
artifacts, and publishing the assets into catalogs. Some governance artifacts are predefined and are
automatically assigned to data assets. Data stewards can create or import a business vocabulary to
further enrich data assets during data curation. Knowledge Accelerators provide sets of ready to use business
vocabulary for specific industries. You use categories to control who can create and use governance
artifacts for what purpose.
You can create data protection rules that define how to protect data. Data protection rules are
enforced automatically in a uniform manner in governed catalogs. You can configure data protection
rules to mask sensitive data based on the content, format, or meaning of the data, or the identity
of the users who access the data. When you mask data, you unlock the data for users who are not
authorized to view sensitive data and avoid the need to maintain multiple copies of the data.
You provide a self-service way to find and share assets across your enterprise with catalogs:
Collaborators in a catalog have access to data assets without needing separate credentials or
being able to see the credentials. Collaborators have roles that control what activities they can
perform in the catalog.
Data assets contain information about how to access the data, data classifications, assigned
business terms and other governance artifacts, relationships with other assets, and rating and
reviews. Data assets can be relational data or unstructured data, such as PDF or Microsoft Office
documents.
Other types of assets in catalogs include operational assets, which data scientists create with
tools to work with data, such as, models, notebooks, and dashboards.
Search based on data asset metadata and properties and AI-powered recommendations help users
find the data that they need.
Data scientists find assets in catalogs and then copy the assets into projects where they analyze
data and build models with Watson Studio and
Watson
Machine Learning tools.
Use IBM Manta Data
Lineage
for advanced metadata import.
Table 2. Related services. The following related services are often used with this service and
provide complementary features, but they are not required.
Use built-in search, automatic metadata propagation, and simultaneous highlighting of
compilation errors to create, edit, load, and run jobs that transform and tailor information for
your enterprise.
Compatible data sources
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See Connectors for a list of data source services that are compatible.
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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.