Db2 Warehouse on Cloud Pak for Data

IBM Db2 Warehouse is an analytics data warehouse that features in-memory data processing and in-database analytics. It is client-managed and optimized for fast and flexible deployment, with automated scaling that supports analytics workloads. Based on the number of worker nodes selected, IBM Cloud Pak for Data automatically creates the appropriate data warehouse environment. For a single node, the warehouse uses symmetric multiprocessing (SMP) architecture for cost-efficiency. For two or more nodes, the warehouse is deployed using a massively parallel processing (MPP) architecture for high availability and improved performance. Using the Db2 Warehouse operator and containers in Cloud Pak for Data, you can deploy Db2 Warehouse by using a cloud-native model, providing these values:

• Lifecycle management: Similar to a cloud service, it’s easy to install, upgrade, and manage Db2 Warehouse.
• Ability to deploy your Db2 Warehouse database in minutes
• A rich ecosystem: Data Management Console, REST, Graph
• Extended availability of Db2 Warehouse with a multi-tier resiliency strategy
• Support for software-defined storage such as OCS and IBM Spectrum Scale CSI.

Integrating a Db2 Warehouse database with Cloud Pak for Data can be useful in the following situations:

• You have developers who need to create small-scale database management systems for development and test work. For example, if you need to test new applications and data sources in a development environment before you move them to a production environment.
• You want to accelerate line-of-business analytics projects by creating a data mart service that combines a governed data source with analytic techniques.
• You need to deliver self-service analytics solutions and applications that leverage data that is generated from new sources and is ingested directly into the private cloud warehouse.
• You want to migrate a subset of applications or data from an on-premises data warehouse to a private cloud.
• You want to save money or improve performance by migrating on-premises data marts or an on-premises data warehouse to a cloud-native data warehouse.
• You want to support data scientists who are coding and need to store data locally and need to use a logical representation.
• You want to reduce network traffic and improve analytic performance by storing your data near your analytics engine.
• You have multiple departments, and each department requires their own database management system.