Augmenting Db2 and Netezza workloads with watsonx.data

The white paper discusses augmenting Db2 and Netezza workloads with watsonx.data, a transformative approach to handling data workloads. It highlights two approaches: co-existing and augmentation. The co-existing approach involves integrating Db2/Netezza with watsonx.data, enabling seamless interaction between platforms. The augmentation approach identifies workloads that operate more efficiently within the data lakehouse architecture and offloads them to watsonx.data.

Key Points:

1. Co-existing approach: Db2/Netezza integrates with watsonx.data, allowing bidirectional data syncing and querying Iceberg tables in watsonx.data.
2. Augmentation approach: Identify workloads that operate efficiently in watsonx.data, such as ETL, machine learning, semi-structured/unstructured data, and large volumes of historical data.
3. Identifying workloads: Consider factors like data types, SQL dialects, access control, and performance requirements to determine which workloads to augment.
4. Offloading workloads: Involve database model restructuring, access control, data ingestion, script and application, and performance tuning.
5. Database model: Restructure the database model to accommodate multiple layers of data, including raw, transformed, and application-optimized models.

Main Information:

* The document serves as a reference for creating runbooks for specific client use cases.
* The augmentation approach recognizes the limitations of Db2 Warehouse and Netezza in handling certain workloads and offloads them to watsonx.data for more efficient processing.