Watson Query on Cloud Pak for Data as a Service
Watson Query integrates multiple data sources across locations without having to copy and replicate data, and turns all this data into one virtual data view. This read-only data view makes the job of getting value out of your data easy.
Watson Query is fully integrated into Cloud Pak for Data as a Service on IBM Cloud as part of the data fabric. Watson Query provides the data virtualization capabilities of the data fabric architecture.
To get started, create a service instance of Watson Query 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 Watson Query, 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.
|Watson Knowledge Catalog||Create catalogs of curated assets with this secure enterprise catalog management platform that is supported by a data governance framework.|
|Watson Studio||Prepare, analyze, and model data in a collaborative environment with tools for data scientists, developers, and domain experts.|
Compatible data sources
See Supported data sources in Watson Query for a list of data sources that are compatible.