About Data Catalog

The overarching aim of Data Catalog is to create and maintain an inventory of an organization’s data assets across its entire digital landscape, so that data assets are easier to find and trust to drive insightful business decisions by data consumers.

In the Catalog application in CPSH, you can integrate metadata from multiple data sources: databases, data lakes, warehouses, enterprise applications, ETL tools, and BI solutions. Metadata provides information such as the format of the data, the structure of the data, and when assets were created. For example, Metadata synchronization results for jdbc data sources.
Once the metadata is integrated, you can enrich the metadata by adding profiling information, defining the data class, showing sample data, linking the meta data to the business context, showing the lineage, data quality, and more.

About metadata, samples, profiling data, classification, lineage, and more in Catalog asset pages

Catalog asset pages can include detailed information about the data they represent. These details include:

• Metadata: Metadata is the data about data that is ingested in CPSH by registering or integrating a data source. The way to integrate metadata depends on your data source, infrastructure, and required outcome.

  • Azure Data Lake Storage
  • Amazon S3
  • Databricks
  • Google Dataplex
  • Google Cloud Storage
  • JDBC data sources
  • SAP Datasphere Catalog
  • AI models:
    • AWS Bedrock AI
    • AWS SageMaker AI
    • Azure ML
    • Azure AI Foundry
    • Databricks AI models
    • Google Vertex AI
    • MLflow
    • SAP AI Core

• Profiling: Profiling data provides a statistical summary of the data and includes the data type in the data source.

• Sample data: Sample data is a set of randomly collected data from the data source.

• Classification: Classification data provides shows the data class to which an asset has been assigned via the classification process. Knowing the data classification helps give context to your data.

• Diagrams: Diagrams are also called business lineages, traceability diagrams, or summary lineages. They provide a summary view that traces data views from data source to points of use, for example, a business report. Diagrams are useful for tracking the flow of data in CPSH showing links and dependencies.
  

• Technical lineage: Technical lineages provides a detailed view showing the aggregation, manipulation, and transformation of data through ETL tools, files, and ad-hoc SQL.

Data Catalog submenu pages

The following table describes each of the submenu items of the Catalog application.

Page	Description
Overview	The landing page for Catalog. This page is designed to help you quickly and easily find Data Catalog-related assets.
Reports	All report assets.
Data Sets	All Data Set assets shown as a set of tiles or as a table, with their name, description and, if there are any, connections to existing assets in CPSH.
Data Sources	Data sources that are used for data source registrations.
Data Dictionary	All data assets in CPSH.
Technology Assets	All technology assets in CPSH.
Access Requests	The history of your access requests and their status.
Integrations	Allows you to register a data source. This page contains two tabs. The Data Source Registration tab allows you to create a Database or File System asset from which you can start the synchronization of a data source. Use this tab for JDBC, S3, GCS, and ADLS integrations. The Integration Configuration tab allows you to configure all other Metadata, ETL, and BI Integrations and start the synchronization. For example, Synchronize Databricks Unity Catalog, or Create a technical lineage via Edge.