Big Data technologies represent an opportunity to derive new insight from data on System z and to modernize the entire System z infrastructure regarding the following areas:
- Enabling true mixed Hybrid Transaction/Analytical Processing (HTAP) workloads and delivering a single workload-optimized system with Operational Data Store (ODS) capabilities that integrates operations and business critical analytics into one streamlined system, e.g. by using IBM DB2 Analytics Accelerator (IDAA).
- Complementing existing 'traditional' analytical capabilities with Big Data analytics, e.g. by making use of text analytics and Natural Language Processing (NLP) as part of IBM InfoSphere BigInsights.
- Modernizing the zEnterprise Data Warehouse (zEDW) landscape by significantly reducing the number of traditional repositories, such as a landing zone, staging area, System of Record, data marts, cubes, e.g. by leveraging IDAA.
- Exploring and visualizing big data and deriving to business outcome oriented insight prior to complex transformation, e.g. through IBM Watson Explorer.
- Simplifying the often complex and expensive information supply chain, e.g. by using IDAA and through ELT on Hadoop with IBM InfoSphere DataStage Balanced Optimizationfor Hadoop.
- Integrating Hadoop data repositories as part of a System z centric data reservoir, e.g. by leveraging IBM InfoSphere Information Governance Catalog.
This blog serves the purpose to exchange ideas and to better understand the above opportunities and zEnterprise modernization scenarios in the context of real customer requirements and use case scenarios. It should include architecture patterns and deployment models that are derived from real client engagements. The intent is to describe required integration capabilities that play at the intersection between the key products that were mentioned above. Furthermore, the objective is to articulate existing capabilities in DB2 for z/OS, IDAA, InfoSphere BigInsights and related products, but also to understand gaps that need to be addressed.
This blog should facilitate a discussion on modernizing the System z infrastructure as it relates to its monetization aspects and quantifiable business value. Deliverables should be descriptions of client use case scenarios, opportunities for new insight with data on System z, integration and capability gaps, and emerging System z reference architectures.