This course provides a comprehensive exploration of Db2 12.1 performance tuning and optimization, giving relational DBAs the skills needed to manage and improve workload efficiency. Learners begin by reviewing core optimization concepts, examining how Db2 evaluates access paths, uses statistics, and determines efficient execution strategies. They then progress into more advanced topics, including statistical views, optimization classes, query rewrite behavior, registry variables, and the integration of AI-driven optimizer enhancements.The curriculum also covers essential physical design considerations—such as logging strategies, clustering methods, table partitioning, MDC, and MQT design—to help DBAs create structures that support scalable, high-performance query execution. In addition, learners gain hands-on experience with modern monitoring and diagnostic tools, including db2batch, db2mon, db2caem, the Db2 Data Management Console, and core troubleshooting utilities.Throughout the course, participants reinforce their skills through practical exercises that involve analyzing access plans, applying statistical techniques, tuning queries, benchmarking SQL workloads, and diagnosing performance issues using Db2's monitoring and diagnostic capabilities.
This course provides relational DBAs with essential and advanced skills to manage, secure, recover, and optimize IBM Db2 v12.1 environments. Beginning with foundational concepts, participants will learn how to install Db2, configure instances, manage storage, and create database objects using both command-line and GUI tools. The course then expands into practical techniques for data movement, backup and recovery, and high availability configurations, equipping learners to maintain data integrity and ensure business continuity.Security and concurrency are addressed in depth, including the implementation of user access controls, role-based permissions, encryption methods, Row and Column Access Control (RCAC), Label-Based Access Control (LBAC), and Trusted Contexts for multi-tiered architectures. Participants will also explore Db2’s locking mechanisms and isolation levels to troubleshoot contention issues and manage concurrent workloads effectively.Performance tuning and optimization are emphasized through detailed instruction on the query optimizer, indexing strategies, monitoring tools, and the use of EXPLAIN utilities. Learners will leverage the Db2 AI Optimizer, Design Advisor, and Data Management Console to analyze SQL access plans and improve system responsiveness across transactional and analytical workloads.Through hands-on lab exercises and real-world scenarios, learners will gain the proficiency to manage Db2 environments efficiently and securely, diagnose issues quickly, and ensure high availability and performance in enterprise-scale deployments.
Learn about the Information Management System (IMS) High Availability Large Database (HALDB). Examine how databases may be migrated to HALDB and the advantages of using HALDB.This course is taught in a Web conference medium with live instructor audio, and Internet Web conferenced materials. You have chat type Questions and Answers (Q and A) ability, plus live audio. The course is taught 10:00 a.m. to 12 noon and 1:00 p.m. to 3:00 p.m. Central time for two work days. Since the class is taught live using Web conferencing methods, you can attend from your home or work. Class durations are approximately one-half day so you can still accommodate daily work responsibilities.You will be contacted prior to class start to receive connection information, hardcopy materials, and other relevant information.
This course teaches learners how to explore, analyze, and report on data using IBM Planning Analytics Workspace and related clients. Participants learn core Planning Analytics conceptssuch as models, cubes, dimensions, views, and setsand apply them to create interactive reports, dashboards, and formatted outputs. The course combines conceptual instruction with guided, hands-on exercises that allow learners to practice real-world tasks, including creating books and views, formatting reports, entering and spreading data, applying calculations and conditional formatting, working with sandboxes, publishing websheets, and exploring forecasting results. These exercises reinforce best practices and help learners build confidence using Planning Analytics features in practical business scenarios. The course reflects the current Planning Analytics Workspace v2.1.x environment while preserving content that remains valid from earlier v2.x releases. The course emphasizes effective analysis, reporting, collaboration, and planning techniques that learners can immediately apply in their own Planning Analytics implementations.
This course teaches modelers how to design and build a complete model using IBM Planning Analytics Workspace. Learners progress through the full modeling lifecycle, starting with an overview of Planning Analytics architecture and continuing through dimension design, cube construction, data loading, and business rule development. The course emphasizes modeling best practices and performance considerations using Planning Analytics version 2.1.x.Through a combination of instructor-led explanations and hands-on exercises, learners create and enhance a working model that reflects common planning and analysis scenarios. Topics include TurboIntegrator processes for loading and maintaining data, rules and feeders for advanced calculations, and techniques for optimizing model performance.The course also covers advanced modeling capabilities such as drill-through paths, currency conversion, and time modeling for different fiscal requirements. By the end of the course, learners will be able to build scalable, maintainable models that support planning, forecasting, and reporting use cases in Planning Analytics Workspace.
This course provides a high-level overview of the IBM Cognos Analytics v12 Data Module tool and its underlying architecture to provide learners with the skills necessary to master data modeling using the web-based, self-service capabilities of IBM Cognos Analytics v12 Data Modules. Learners will explore the essential steps in building data modules, from understanding the purpose and workflow of data modeling to creating, refining, and optimizing data structures. The course covers various topics, including managing relationships, advanced features and performance optimization, data enrichment with calculations and filters, creating groups and navigation paths, and managing data security. Learners will gain hands-on experience through exercises that enhance their ability to create and customize data modules, review and modify column properties, creating calculations, filters and data groups, and adding column dependencies. These hands on activities ensure learners practice consistency and usability for reporting, dashboarding, and exploration purposes.By the end of this course, learners should be proficient in developing data modules that integrate multiple data sources, support business analysis, and facilitate informed decision-making within the IBM Cognos v12 environment.
This course provides a comprehensive introduction to building interactive, insightful dashboards and stories using IBM Cognos Analytics version 12. Through a combination of demonstrations, hands-on practice, and best practices, you will learn how to create compelling visualizations, develop dynamic stories, build explorations for advanced analysis, and apply forecasting and drill-through techniques to enhance user interactivity. The course covers both foundational and advanced features, including new visualization properties, customization options, and predictive insights. By the end of the course, you will be able to design professional, data-driven dashboards and deliver actionable insights to support better business decision-making.
This course discusses the differences between AIX and Linux by comparing and contrasting the two operating systems. System administration is focus of this course, so typical tasks performed by a system administrator are covered. All comparisons are targeted towards the Red Hat Linux (RHEL) operating system (running on IBM Power servers). However, in many cases the concepts discussed here also apply to other Linux distributions (that run natively on IBM Power servers). This is a jumpstart for Linux class for experienced AIX admins. The material is not intended as a complete replacement or substitute for more comprehensive Linux technical training provided by IBM and Linux vendors. At the end of the course students will have learned the foundation of many Linux administration tasks, enabling them to start their journey with Linux system administration and expand their skills moving forward. Students are then encouraged to attend further training to deepen their native Linux system administration skills.
This 2-day instructor-led course helps the students to understand how to modify existing applications and create new ones in Maximo Manage. This also helps them gain a comprehensive understanding of the different types of applications available, their functionalities, and how to configure them effectively. This course covers various domain types in Maximo, including how to use and configure Lookups within the applications. The course will also revisit key concepts of Database Configuration, focusing on essential aspects required for application modification and creation. Additionally, the students will learn how to set up the environment, configure the properties, and manage the user access based on specific conditions, and will delve into the use of Signature Options and User Information to enhance application functionality. Through a combination of procedural information, demonstrations, and exercises, students will learn about topics such as Domains, Database Configuration, Application export and import, and Application Designer along with hands-on lab exercises using your own Red Hat OpenShift Maximo Manage environment.
This 2-day instructor-led course provides students with an understanding of some of the more advanced work management techniques found in IBM MAS Manage, Reliability Strategies, Work Order Intelligence, Dynamic Job Plans, and Process Flow Control. We will also see how to edit and create Operational dashboards and Work Queues. After learning about these concepts, practice what you have learned by doing the hands-on lab exercises.
This course teaches learners how to explore, analyze, and report on data using IBM Planning Analytics Workspace and related clients. Participants learn core Planning Analytics concepts—such as models, cubes, dimensions, views, and sets—and apply them to create interactive reports, dashboards, and formatted outputs. The course combines conceptual instruction with guided, hands-on exercises that allow learners to practice real-world tasks, including creating books and views, formatting reports, entering and spreading data, applying calculations and conditional formatting, working with sandboxes, publishing websheets, and exploring forecasting results. These exercises reinforce best practices and help learners build confidence using Planning Analytics features in practical business scenarios. The course reflects the current Planning Analytics Workspace v2.1.x environment while preserving content that remains valid from earlier v2.x releases. The course emphasizes effective analysis, reporting, collaboration, and planning techniques that learners can immediately apply in their own Planning Analytics implementations.
This course teaches modelers how to design and build a complete model using IBM Planning Analytics Workspace. Learners progress through the full modeling lifecycle, starting with an overview of Planning Analytics architecture and continuing through dimension design, cube construction, data loading, and business rule development. The course emphasizes modeling best practices and performance considerations using Planning Analytics version 2.1.x.Through a combination of instructor-led explanations and hands-on exercises, learners create and enhance a working model that reflects common planning and analysis scenarios. Topics include TurboIntegrator processes for loading and maintaining data, rules and feeders for advanced calculations, and techniques for optimizing model performance.The course also covers advanced modeling capabilities such as drill-through paths, currency conversion, and time modeling for different fiscal requirements. By the end of the course, learners will be able to build scalable, maintainable models that support planning, forecasting, and reporting use cases in Planning Analytics Workspace.
The goal of this course is to provide the student with a tangible understanding and a real hands-on experience with generative AI applications deployed and optimized for IBM Power systems. This self-paced virtual course uses video lectures, review questions, and virtual lab machine exercises to provide the student with foundational knowledge and experience about the topics covered in the course. In the lecture, the student begins by learning the basics of generative AI, and the basic components of a generative AI application, then applies these concepts to real-world examples on Power where the student learns Power offerings for AI workloads, package management fundamentals in Python, Operating System deployment strategies on Power, and Power hardware use cases. The lab exercises will start with viewing and manipulating files in a basic AI application running on IBM Power Red Hat Enterprise Linux. They will have hands-on experience identifying the components of that AI application and their connection to other components, and will add functionalities to the application that demonstrate more advanced AI application techniques like frameworks, prompt tuning, and conversation memory. Then, the students will go through the process of setting up a Python virtual environment, investigating Power hardware resources, and using the Hardware Management Console (HMC) to ensure memory and other resources are optimized for AI workloads.
In this course, you learn how to deploy IBM Verify Directory. You gain skills around deploying the software, deploying and using Virtual Directory Server, and deploying and using Federated Directory Server instead of replication. This course also provides examples of LDAP commands for adding, modifying, and deleting users from the directory.
This course is designed to provide participants with a comprehensive understanding of how to plan, deploy, configure, and manage IBM Storage Ceph in enterprise environments. Through lectures, demonstrations, and extensive hands-on exercises, students will gain practical skills for working with Ceph’s distributed storage architecture. Key topics include cluster deployment, administration, data protection, and performance optimization, as well as using Ceph for object, block, and file storage workloads. By the end of the course, students will be equipped to integrate Ceph into enterprise infrastructures and operate it effectively in production environments.
IBM Storage Defender Data Protect is a comprehensive data backup and recovery solution that supports a wide range of cloud-native, edge, and on-premises workloads at scale. It helps organizations meet governance and compliance requirements for data protection and incident response, significantly reducing operational and cyber risk.In this one-day course, students learn how to deploy and perform initial maintenance tasks for an IBM Storage Defender Data Protect cluster. Hands-on lab exercises are conducted using a Linux-based environment.
Learn the essentials of IBM Fusion - its architecture, hardware, networking, data services, UI, virtualization, and multi-cluster management. This course earns you the IBM Fusion Essentials badge: https://www.credly.com/org/ibm/badge/ibm-fusion-essentials.
This course is designed to teach you how to do important post-installation administration and configuration tasks for the IBM Cloud Pak for AIOps platform. Such tasks include user management, importing topology, configuring self-monitoring, configuring correlations, and making runbooks to expedite or automate solutions to IT problems.
This course is segmented into multiple learning modules. Module 1 is an overview discussing what RIA is and how it can help to automate tasks across a business. Module 2 is configuration & UI navigation covering how to configure API authentications in RIA and how to find the main sections of the user interface. Module 3 is basic programming in RIA covering some of the commonly used programming blocks that are combined with API blocks to create automation workflows. Module 4 is workflows covering workflows in their entirety and how they're built, saved, and published. Module 5 is integrations in workflows covering product integrations in RIA and how they're used in workflows. Module 6 is troubleshooting covering some of the more common issues with workflows in RIA and how to approach fixing them. All of these learning modules contain step by step walkthroughs that have learners demonstrate their ability to complete learning objectives in a guided session.
This course is segmented into multiple learning modules. After an introduction, each Data Insight core widget will be covered in its own module, and a few administrative tasks will round out the training.