Showing posts with label ML. Show all posts
Showing posts with label ML. Show all posts

Thursday, August 14, 2025

Machine Learning and AI Integration in Db2 for z/OS

In today’s data-driven world, the ability to harness the power of machine learning (ML) and artificial intelligence (AI) is essential for organizations aiming to stay competitive. With the introduction of Db2 for z/OS Version 13 and subsequent function levels, IBM has made significant strides in integrating ML and AI capabilities directly into the Db2 ecosystem, transforming the way businesses leverage their data.

SQL Data Insights

Perhaps the single most important new AI capability added to Db2 13 for z/OS is SQL Data Insights (SDI). I have written about this before and if you are interested in a more thorough discussion of SDI, check out this article on elnion.

At a high level though, SDI enables data scientists and analysts to run advanced analytics directly on data residing in Db2 without the need for extensive data movement. By minimizing data transfer, organizations can reduce latency and improve the efficiency of their workflows.

The initial support for SDI in Db2 13 for z/OS FL600 included three AI functions: AI_SIMILARITY, AI_SEMANTIC_CLUSTER and AI_ANALOGY. Function level 504 added a fourth: AI_COMMONALITY.

Python Support

Python is the dominant programming language for AI and ML because of its simplicity, readability, and vast ecosystem of libraries. It offers clear syntax allowing data scientists and developers to focus on solving problems rather than wrestling with complex code structures. This makes it ideal for rapid prototyping of AI models. Rich frameworks such as TensorFlow, PyTorch, and others provide ready-to-use tools for data preparation, model training, and evaluation, significantly reducing development time. Moreover, Python’s large, active community continually contributes new algorithms, techniques, and integrations, ensuring that it stays at the forefront of AI and ML innovation. This combination of usability, flexibility, and ecosystem maturity has made Python the de facto standard for building, deploying, and operationalizing AI and ML solutions across industries.

With Python being so important to data scientists, it stands to reason that IBM should support it in Db2 for z/OS. And they do! Python support for Db2 for z/OS was delivered with the IBM Db2 AI for z/OS and the Db2 for z/OS Python driver as part of the IBM Db2 for z/OS “Data Server Driver for ODBC, CLI, and .NET” family.

  • IBM Db2 AI for z/OS (Db2ZAI) is an advanced solution designed to enhance the operational performance, reliability, and efficiency of Db2 for z/OS systems. By leveraging machine learning (ML) and artificial intelligence (AI), it improves many aspects of Db2 management. We will discuss it in a little more detail in the next section.
  • The Python driver is IBM's official database connectivity driver that allows Python applications to connect to and interact with IBM DB2 databases. It delivers connectivity not just for Db2 for z/OS, but also for other IBM database products including DB2 for Linux/Unix/Windows, DB2 for i (AS/400), and IBM Informix.

So, Python support became generally available via IBM Db2 for z/OS Distributed Data Facility (DDF) using the IBM Data Server Driver for Python, which is the same Python driver used for Db2 LUW, but configured to connect over DRDA to Db2 for z/OS.

This wasn’t tied to a specific Db2 function level—rather, it was an enhancement to the client connectivity stack and supported back to Db2 11 for z/OS with the right PTFs. Of course, as of this December (2025) Version 13 will be the only supported version of Db2 for z/OS.

Machine Learning Enhanced Optimization

The Db2 optimizer can also benefit from an infusion of AI. Optimization improvement is a benefit of IBM’s Db2 AI for z/OS, an add-on solution that uses AI/ML to elevate system operations and performance.

IBM Db2 AI for z/OS continuously analyzes workload patterns, system metrics, and SQL execution behavior to recommend or automatically apply optimizations—such as selecting better access paths, tuning buffer pools, or adjusting configuration settings to reduce CPU usage. By learning from an organization’s actual Db2 workload over time, it adapts its recommendations to evolving data and usage patterns, helping maintain consistent performance without constant manual tuning.

In addition, Db2 AI for z/OS can assist in workload management, anomaly detection, and operational decision-making, giving DBAs intelligent, data-driven insights to run large-scale mainframe database systems more efficiently. By incorporating machine learning into key processes it can help to reduce CPU usage, optimize SQL query plans and concurrency, and detect and resolve anomalies and root causes.

Indeed, the AI-driven operational support of Db2 AI for z/OS goes beyond using AI in SQL queries. It is focused on keeping Db2 for z/OS environments running optimally and proactively, enhancing system resiliency and availability.

Summing Things Up

IBM continues to integrate machine learning and AI capabilities into Db2 for z/OS. By empowering organizations to leverage their data for predictive analytics and advanced machine learning, IBM is helping businesses unlock new opportunities and drive smarter decision-making. As these technologies continue to advance, the potential for innovation and growth in the data landscape is limitless. Embrace the future of data with Db2 for z/OS and unleash the power of AI and machine learning in your organization today!

Monday, November 11, 2024

5 Big Concerns of Modern IT When Using Db2 for z/OS

Db2 for z/OS is an entrenched solution for managing data at the world's largest organizations. It is a strong, reliable DBMS and I wrote about its strength recently on the blog (here). You really cannot go wrong using Db2 for z/OS for mission-critical workloads.

That said, there are concerns and issues facing organizations using Db2 for z/OS. One of the biggest concerns with Db2 for z/OS today is managing the cost and complexity of maintaining mainframe environments while still delivering high availability and performance. 

As such, here are 5 specific concerns facing large organizations using Db2 for z/OS today:

  1. Skill Shortages: Many mainframe experts, especially those with deep Db2 for z/OS knowledge, are approaching retirement, creating a significant skills gap. The lack of trained professionals has made it challenging to manage and maintain Db2 for z/OS systems effectively.

  2. Cost of Licensing and Maintenance: Mainframe systems come with substantial licensing costs. Many organizations are looking for ways to optimize usage or even repatriate workloads to more cost-effective platforms, where feasible, to reduce operational expenses. Whether or not such changes result in "actual" cost reductions is unfortunately irrelevant as many executives believe it will regardless of reality and studies to the contrary.

  3. Integration with Modern Architectures: As companies adopt cloud, big data, and other modern architectures, integrating Db2 for z/OS with these systems can be complex and costly. Many seek seamless data integration between Db2 on mainframes and newer platforms like data lakehouses, which involves architectural and technological challenges.

  4. Automation and DevOps Compatibility: Modern IT environments emphasize agility, continuous integration, and deployment, but the mainframe environment traditionally doesn’t integrate well with DevOps practices. Nevertheless, many companies are pushing for Db2 automation tools and integration with DevOps workflows to streamline operations and reduce manual workloads... and DevOps is being successfully deployed by mainframe organizations today using Zowe and other traditional DevOps tooling.

  5. Performance and Availability: High performance and availability are always top concerns, especially as organizations process more data and need to meet stringent SLAs. Handling lock contention, optimizing query performance, and scaling resources efficiently continue to be challenges. But, to be fair, these are challenges with many DBMS implementations, not just Db2 for z/OS.

Organizations are adopting several strategies to address the challenges with Db2 for z/OS and ensure their mainframe environments remain relevant and efficient:

  1. Workforce Development and Knowledge Transfer: To counter skill shortages, organizations are investing in training and upskilling initiatives for new IT staff, partnering with universities, or using mentoring programs to transfer knowledge from retiring mainframe experts to newer employees. Additionally, some companies are leveraging consulting firms or managed services providers with mainframe expertise to fill gaps temporarily.

  2. Cost Optimization with Usage Analytics: Companies are using detailed workload and resource monitoring tools to optimize Db2 for z/OS usage, identify inefficient processes, and reduce costs. This includes tuning queries, scheduling batch jobs during off-peak hours, and leveraging IBM’s Workload Manager (WLM) to prioritize workloads based on business needs.

  3. Hybrid Cloud and Data Lakehouse Integrations: To manage integration with modern architectures, organizations are implementing hybrid cloud strategies and data lakehouses that can interface with Db2 for z/OS. Tools such as IBM Db2 Analytics Accelerator allow data stored on Db2 for z/OS to be offloaded to faster, scalable platforms, enabling integration with big data and analytics environments without entirely migrating off the mainframe.

  4. Automation and DevOps Integrations: Organizations are investing in DevOps and automation tools compatible with Db2 for z/OS, such as IBM UrbanCode and mainframe DevOps solutions from other ISVs such as Broadcom and BMC Software. By automating routine tasks like provisioning, patching, and deploying schema changes, organizations can adopt more agile, efficient processes. Integrating Db2 for z/OS with CI/CD pipelines helps streamline development workflows, bridging mainframe operations with modern DevOps practices. For more details on integrating Db2 for z/OS into DevOps, consult this blog post that highlights several posts I wrote on the topic!

  5. Mainframe Modernization with AI and Machine Learning: Using AI and machine learning to optimize Db2 for z/OS operations is becoming common. AI-based monitoring tools, such as IBM’s Watson AIOps, can predict system issues and detect anomalies to prevent downtime. Machine learning algorithms can also be used for capacity planning, workload optimization, and tuning Db2 performance parameters, helping reduce manual intervention.

  6. Resilience and High Availability Improvements: For performance and availability, companies are implementing high-availability solutions like IBM Geographically Dispersed Parallel Sysplex (GDPS) to ensure continuous uptime. They’re also using backup automation and disaster recovery solutions tailored for Db2 to meet stringent SLAs and minimize downtime in case of failures.

By combining these strategies, organizations are better equipped to manage the costs, complexity, and skills required to maintain and modernize Db2 for z/OS environments in today’s rapidly evolving IT landscape.

Wednesday, October 21, 2020

Automation and the Future of Modern Db2 Data Management

Recently I was invited by BMC Software to participate in their AMI Z Talk podcast series to talk about modern data management for Db2... and I was happy to accept.

Anne Hoelscher, Director of R+D for BMC's Db2 solutions, and I spent about 30 minutes discussing modern data management, the need for intelligent automation, DevOps, the cloud, and how organizations can achieve greater availability, resiliency, and agility managing their mainframe Db2 environment.

Here's a link to the podcast that you can play right here in the blog!


Modern data management, to me, means flexibility, adaptability, and working in an integrated way with a team. Today’s data professionals have to move faster and more nimbly than ever before. This has given rise to agile development and DevOps - and, as such, modern DBAs participate in development teams. And DBA tasks and procedures are integrated into the DevOps pipeline. 

And as all of this DevOps adoption is happening, the amount of data we store, and have to manage, continues to grow faster than ever before.

These are just some of the challenges that Anne and I discuss in this podcast... and at the end, Anne even asks me to predict the future... 

I hope you'll take the time to listen to our discussion and sharing your thoughts and issues regarding the resiliency and agility required to succeed with modern data management and Db2 for z/OS.

----------

I’d also like to extend an offer to all the listeners of this BMC podcast (and readers of this blog post) to get a discount on my latest book, A Guide to Db2 Performance for Application Developers. The link is https://tinyurl.com/craigdb2

There’s also a link to the book publisher on home page of my website. Once you are there, click on the link/banner for the book and when you order from the publisher you can use the discount code 10percent to get 10% off your order of the print or ebook.