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Duration 1 Days 6 CPD hours This course is intended for The primary audience for this course is database developers who plan to migrate their MySQL or Postgres DB workloads to Azure SQL DB. The secondary audience for this course is MySQL/Postgres administrators to raise awareness of the features and benefits of Azure SQL DB. Overview At the end of this course, the students will have learned: Migrate on-premises MySQL to Azure SQL DB for MySQL Migrate on-premises PostgreSQL to Azure SQL DB for PostgreSQL This course will enable the students to understand Azure SQL Database, and educate the students on what is required to migrate MySQL and PostgreSQL workloads to Azure SQL Database. Migrate to Azure SQL DB for MySQL & PostgreSQL OSS databases overview Common OSS database workloads Customer challenges in migration Migrate on-premises MySQL to Azure SQL DB for MySQL Configure and Manage Azure SQL DB for MySQL Migrate on-premises MySQL to SQL DB for MySQL Application Migration Post-migration considerations Migrate on-premises PostgreSQL to Azure SQL DB for PostgreSQL Configure and Manage Azure SQL DB for PostgreSQL Migrate on-premises MySQL to SQL DB for PostgreSQL Application Migration Post-migration considerations

Duration 0.5 Days 3 CPD hours This course is intended for This course is designed for students who have experience using the Windows 10 operating system and need to start using the Windows 11 operating system. Overview In this course, you will use the new and updated features of Windows 11. You will: Navigate the Windows environment. Use apps available in Windows 11. Manage available apps. Configure Windows 11 settings. As an experienced Windows© 10 user, when you are ready to move to Windows 11, you might like some guidance in using the new and updated features. This course will help you identify and use those features efficiently and effectively. Navigating the Windows 11 Environment Topic A: Log in to Windows 11 Topic B: Use the Start Menu Topic C: Use the Taskbar Using Apps Topic A: Use Built-In Apps Topic B: Use the Updated File Explorer Managing Apps Topic A: Use Virtual Desktops Topic B: Obtain Apps from the Microsoft Store Configuring Windows 11 Settings Topic A: Use the Configuration Apps Topic B: Configure Accessibility Features

Duration 4 Days 24 CPD hours This course is intended for This course is designed for technical professionals who require the skills to administer IBM© MQ queue managers on distributed operating systems, in the Cloud, or on the IBM© MQ Appliance. Overview After completing this course, you should be able to:Describe the IBM© MQ deployment optionsPlan for the implementation of IBM© MQ on-premises or in the CloudUse IBM© MQ commands and the IBM© MQ Explorer to create and manage queue managers, queues, and channelsUse the IBM© MQ sample programs and utilities to test the IBM© MQ networkEnable a queue manager to exchange messages with another queue managerConfigure client connections to a queue managerUse a trigger message and a trigger monitor to start an application to process messagesImplement basic queue manager restart and recovery proceduresUse IBM© MQ troubleshooting tools to identify the cause of a problem in the IBM© MQ networkPlan for and implement basic IBM© MQ security featuresUse accounting and statistics messages to monitor the activities of an IBM© MQ systemDefine and administer a simple queue manager cluster This course provides technical professionals with the skills that are needed to administer IBM© MQ queue managers on distributed operating systems and in the Cloud. In addition to the instructor-led lectures, you participate in hands-on lab exercises that are designed to reinforce lecture content. The lab exercises use IBM© MQ V9.0, giving you practical experience with tasks such as handling queue recovery, implementing security, and problem determination. Note: This course does not cover any of the features of MQ for z/OS or MQ for IBM© i. Course introductionIBM© MQ reviewIBM© MQ installation and deployment optionsCreating a queue manager and queuesExercise: Using commands to create a queue manager and queuesIntroduction to IBM© MQ ExplorerExercise: Using IBM© MQ Explorer to create queue managers and queuesTesting the IBM© MQ implementationExercise: Using IBM© MQ sample programs to test the configurationImplementing distributed queuingExercise: Connecting queue managersIBM© MQ clientsExercise: Connecting an IBM© MQ clientImplementing trigger messages and monitorsExercise: Implementing a trigger monitorDiagnosing problemsExercise: Running an IBM© MQ traceImplementing basic security in IBM© MQExercise: Controlling access to IBM© MQBacking up and restoring IBM© MQ messages and object definitionsExercise: Using a media image to restore a queueExercise: Backing up and restoring IBM© MQ object definitionsIntroduction to queue manager clustersExercise: Implementing a basic clusterMonitoring and configuring IBM© MQ for performanceExercise: Monitoring IBM© MQ for performanceCourse summary

Duration 5 Days 30 CPD hours This course is intended for Administrator Architect Database Administrator Overview To provide an acceptable response time to users and manage resources effectively, you learn how to monitor performance and manage resources within the multitenant container database and its pluggable databases, and within each pluggable database. Another important aspect is the data movement between non-CDBs and pluggable databases, and between pluggable databases. It is also important to understand the procedures of upgrading an Oracle Database multitenant container database or an Oracle Database pluggable database. Finally, students discover the way multitenant container database and pluggable databases are created and monitored in the Cloud. This course covers all aspects of the multitenant architecture, providing detailed information on the components of an Oracle multitenant container database and its regular and application pluggable databases. You learn why and how to create and manage a multitenant container database and its regular and application pluggable databases, with storage structures appropriate for the business applications. You practice cold and hot cloning, plugging unplugged pluggable databases in multitenant container databases using various methods. CDB BasicsCDB and Regular PDBsApplication PDBs and Application InstallationPDB CreationCDB and PDB ManagementStorageSecurityBackup and DuplicateRecovery and FlashbackPerformance MonitoringResources AllocationData MovementUpgrade methods

Duration 5 Days 30 CPD hours This course is intended for Horizon Cloud Service on Microsoft Azure administrators, system integrators, account managers, solutions architects, solutions engineers, sales engineers, and consultants. Overview By the end of the course, you should be able to meet the following objectives: Describe the architecture of Horizon Cloud Service on Microsoft Azure Discuss the initial Microsoft Azure configurations required for the Horizon Cloud Service on Microsoft Azure deployment Discuss Horizon Cloud Service on Microsoft Azure networking concepts Discuss Horizon Cloud Service on Microsoft Azure AD requirements and integration best practices Determine steps and requirements to deploy or upgrade Horizon Cloud Service on Microsoft Azure Recognize Horizon Cloud Service console controls that are available for administrators Identify Horizon Cloud upgrade features and benefits List the steps and considerations to take when setting up a primary VM to be used as an assignable image Identify how to access desktops and application from Horizon Cloud Service on Microsoft Azure Discuss and create Remote Desktop Session Host Farms Explain power management options in the RDSH farm Create VDI desktop assignments and entitlements Manage assignable images on Horizon Cloud Service on Microsoft Azure Describe and Use Image management service for Horizon Cloud Service on Microsoft Azure Describe the integration of Dynamic Environment Manger with Horizon Cloud Service on Microsoft Azure Manage user personalization and application configurations using the Dynamic Environment Manager management console and application profiler Discuss the usage of App Volumes for Horizon Cloud Service on Microsoft Azure Discuss the integration of Workspace ONE Access with Horizon Cloud Service on Microsoft Azure Interpret scalability considerations for Horizon Cloud Service on Microsoft Azure Determine the process of deploying, configuring, and paring Horizon Cloud Connector into your pod's environment Apply troubleshooting techniques relevant to Horizon Cloud Service and Microsoft Azure Summarize the analytics and monitoring capabilities in Horizon Cloud Service on Microsoft Azure This five-day, hands-on training provides you with the knowledge, skills, and abilities to achieve competence in deploying and managing VMware Horizon© Cloud Service? on Microsoft Azure. This training increases your skills and competence in using the VMware Horizon© Cloud Administration Console and Microsoft Azure portal.Through a combination of hands-on labs and interactive lectures, you learn how to import and manage images for VDI and RDSH assignments. You also learn how to configure and use the Universal Broker function, VMware App Volumes?, Workspace ONE Access and VMware Dynamic Environment Manager? in the Horizon Cloud Service on Microsoft Azure deployment. Course Introduction Introduction and course logistics Course objectives Introduction to Horizon Cloud Service on Microsoft Azure Identify Horizon Cloud Service on Microsoft Azure features, benefits, and licensing options Interpret Horizon Cloud Service on Microsoft Azure architecture components to identify configuration prerequisite Interpret Horizon Cloud Service on Microsoft Azure deployment models Discuss the initial Microsoft Azure configurations required for the Horizon Cloud Service on Microsoft Azure deployment Microsoft Azure Networking Requirements Summarize Horizon Cloud connectivity considerations and tasks Discuss Horizon Cloud on Microsoft Azure networking concepts Identify ports required for local connections, remote connections, and endpoint operating system firewall rules Active Directory List the features and limitations of supported AD configurations Discuss Horizon Cloud Service on Microsoft Azure AD integration best practices Determine Horizon Cloud Service on Microsoft Azure AD requirements Deployment and Upgrades Determine steps and requirements to deploy Horizon Cloud Service on Microsoft Azure Discuss the features and benefits of using multiple tenant subnets for desktops and RDSH Discuss the features and benefits of using Internal and External UAG Recognize Horizon Cloud Service console controls that are available for administrators Identify Horizon Cloud upgrade features and benefits Creating Images Outline the process and choices to set up primary VMs Identify the configuration choices for importing primary VMs List steps to install the user software on the primary VM Identify steps to convert a configured primary VM to an assignable image Access Desktops and Applications Use Horizon Client to access desktops and remote applications Compare the remote display protocols that are available for Horizon Cloud Remote Desktop Session Host Farms List the steps and considerations to take when creating an RDSH farm List the actions that can be performed on farms listed on the console?s Farms page List the actions to assign an application to a user or group List the prerequisites and steps to create an RDSH session assignment VDI Desktops Compare a dedicated assignment to floating assignment Outline steps to create a VDI desktop assignment Explain the entitlement of desktops Managing Assignable Images Describe and manage assignable images Describe and Use Image management service for Horizon Cloud Service on Microsoft Azure VMware Dynamic Environment Manager Identify the VMware Dynamic Environment Manager functional areas and their benefits Prepare an infrastructure for VMware Dynamic Environment Manager Outline the steps that are required to install and configure Dynamic Environment Manager components Manage user personalization and application configurations using the Dynamic Environment Manager management console and application profiler App Volumes for Horizon Cloud Service on Microsoft Azure Explain how App Volumes works with Horizon Cloud Service on Microsoft Azure Identify the features and benefits of App Volumes in Horizon Cloud Service on Microsoft Azure Identify the interface elements of App Volumes in Horizon Cloud Service on Microsoft Azure Install and configure App Volumes in Horizon Cloud Service on Microsoft Azure Workspace ONE Access Describe the benefits of integrating VMware Horizon Cloud service with Workspace ONE Access. Describe how a user obtains access to an entitled virtual desktop or application from the Intelligent Hub catalog. Describe the importance of binding directory with Workspace ONE Access and setting up custom user attribute synchronization. Describe the importance of configuring the Remote App Access Client in Workspace ONE Access. Summarize the steps of configuring the Access settings in Horizon Cloud control panel. Access an entitled Horizon virtual desktop or application in the Intelligent Hub catalog. Scalability Considerations Discuss the Horizon Cloud Service on Microsoft Azure scalability cost and settings Describe the usage of Universal Broker in Horizon Cloud Service on Microsoft Azure Horizon Cloud Connector Describe the features and benefits of Horizon Cloud Connector List the prerequisites and requirements to connect a Horizon pod with Horizon Cloud Connector Determine the process of deploying, configuring, and paring Horizon Cloud Connector into your pod?s environment Troubleshooting Horizon Cloud Service on Microsoft Azure Discuss Horizon Cloud Service on Microsoft Azure troubleshooting basics Discuss Horizon Cloud Service troubleshooting basics Summarize the analytics and monitoring capabilities in Horizon Cloud Service on Microsoft Azure

Duration 4 Days 24 CPD hours This course is intended for This is an intermediate course for experienced DBAs and technical individuals, with experience on other relational database platforms, who plan, implement, and maintain Db2 11.1 for Linux, UNIX, and Windows databases. These skills can also be utilize to support cloud based databases using Db2 on Cloud or Db2 Hosted environments. Overview Please refer to course overview This course teaches you to perform, basic and advanced, database administrative tasks using Db2 11.1. These tasks include creating and populating databases and implementing a logical design to support recovery requirements. The access strategies selected by the Db2 Optimizer will be examined using the Db2 Explain tools. Various diagnostic methods will be presented, including using various db2pd command options. Students will learn how to implement automatic archival for database logs and how to plan a redirected database restore to relocate either selected table spaces or an entire database. The REBUILD option of RESTORE, which can build a database copy with a subset of the tablespaces, will be discussed. We will also cover using the TRANSPORT option of RESTORE to copy schemas of objects between two Db2 databases. The selection of indexes to improve application performance and the use of SQL statements to track database performance and health will be covered. This course provides a quick start to Db2 database administration skills for experienced relational Database Administrators (DBA). Overview of Db2 11Command Line Processor (CLP) and GUI UsageThe Db2 EnvironmentCreating Databases and Data PlacementCreating Database ObjectsMoving DataBackup and RecoveryLocks and ConcurrencyDatabase Maintenance, Monitoring and Problem DeterminationSecurityDatabase Rebuild SupportDb2 Database and Table Space RelocationUsing Explain ToolsUsing Indexes for PerformanceAdvanced Monitoring

Duration 5 Days 30 CPD hours This course is intended for Data Warehouse AdministratorDatabase AdministratorsDatabase DesignersSupport EngineerTechnical Administrator Overview Monitor the DatabaseManage Database PerformanceImplement Database AuditingConfigure the Database Instance Such That Resources Are Appropriately Allocated Among Sessions and TasksSchedule Jobs to Run Inside or Outside of the DatabaseConfigure Oracle Net ServicesConfigure your Database For Backup and Recovery OperationsDescribe Oracle Database ArchitectureManage the Oracle Database InstanceManage Oracle Database Storage structuresCreate and Administer User Accounts The Oracle Database 12c: Administration Workshop will teach you about the Oracle Database architecture. You will discover how to effectively manage an Oracle Database instance, configure the Oracle Network Environment and perform database maintenance. The Oracle Database 12c: Administration Workshop will teach you about the Oracle Database architecture. You will discover how to effectively manage an Oracle Database instance, configure the Oracle Network Environment and perform database maintenance.

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies