Unlike the cloud-native versions that follow, 11.7.1.3 relies on a classic three-tier architecture: the Client tier (Designer), the Metadata Repository tier (Db2 or Oracle), and the Engine tier (the DataStage parallel engine).
Based on IBM Fix List documents, this fix pack addressed several specific issues: datastage 11.7.1.3
| APAR ID | Issue Resolved | Impact Level | | :--- | :--- | :--- | | | Memory fragmentation in the Parallel Engine causing "Out of Memory" on large sort operations. | High | | JR60812 | WebSphere Application Server (WAS) deadlock when concurrently running 50+ real-time services. | Critical | | JR61002 | Incorrect date conversion between UNIX epoch and Oracle Timestamp when using the Transformer stage. | Medium | | JR61355 | Security vulnerability in the Information Server Console (CVE-2018-11776). | Security | | JR61478 | DataStage Director log retrieval fails when job names contain Unicode characters. | Low | Unlike the cloud-native versions that follow, 11
One of the core strengths of 11.7.1.3 is the . This feature allows DataStage to push down processing to the source or target database (e.g., IBM Netezza, Oracle, or Teradata). In 11.7.1.3, IBM significantly reduced memory leaks in the optimizer, making it more reliable for complex star-schema transformations. | Critical | | JR61002 | Incorrect date
Jobs created in the legacy thick client can be opened and edited in the new Flow Designer without a formal migration process. 2. Technical Enhancements in 11.7.1.3
Support for deploying DataStage Dockers on OpenShift and Google Cloud , with the ability to scale compute pods directly through the Flow Designer.
Given that 11.7.1.3 is aging (released roughly in 2019-2020), you should be planning a migration. Here are your three options: