2990 Software Development courses in Cardiff delivered Online

Duration 5 Days 30 CPD hours This course is intended for Security Professionals working with Kubernetes Clusters Container Orchestration Engineers DevOps Professionals Overview In this course, students will learn and practice essential Kubernetes concepts and tasks in the following sections: Cloud Security Fundamentals Cluster Hardening System Hardening Minimize Microservice Vulnerabilities Supply Chain Security Disaster Recovery Secure Back-up and Restore This class prepares students for the Certified Kubernetes Security Specialist (CKS) exam. Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stabilitywhile maximizing resource utilization for applications and services. By the conclusion of this hands-on, vendor agnostic training you will be equipped with a thorough understanding ofcloud security fundamentals, along with the knowledge, skills and abilities to secure a Kubernetes cluster, detect threats, and properly resolve a security catastrophe. This courseincludes hands-on instruction which develops skills and knowledge for securing container-based applications and Kubernetes platforms, during build, deployment, and runtime. We prioritizecovering all objectives and concepts necessary for passing the Certified Kubernetes Security Specialist (CKS) exam. You will be provided the components necessary to assemble your ownhigh availability Kubernetes environment and harden it for your security needs. Learning Your Environment Underlying Infrastructure Using Vim Tmux Cloud Security Primer Basic Principles Threat Analysis Approach CIS Benchmarks Securing your Kubernetes Cluster Kubernetes Architecture Pods and the Control Plane Kubernetes Security Concepts Install Kubernetes using kubeadm Configure Network Plugin Requirements Kubeadm Basic Cluster Installing Kubeadm Join Node to Cluster Kubeadm Token Manage Kubeadm Tokens Kubeadm Cluster Upgrade Securing the kube-apiserver Configuring the kube-apiserver Enable Audit Logging Falco Deploy Falco to Monitor System Calls Enable Pod Security Policies Encrypt Data at Rest Encryption Configuration Benchmark Cluster with Kube-Bench Kube-Bench Securing ETCD ETCD Isolation ETCD Disaster Recovery ETCD Snapshot and Restore Purge Kubernetes Purge Kubeadm 3Purge Kubeadm Image Scanning Container Essentials Secure Containers Creating a Docker Image Scanning with Trivy Trivy Snyk Security Manually Installing Kubernetes Kubernetes the Alta3 Way Deploy Kubernetes the Alta3 Way Validate your Kubernetes Installation Sonobuoy K8s Validation Test Kubectl (Optional) Kubectl get and sorting kubectl get kubectl describe Labels (Optional) Labels Labels and Selectors Annotations Insert an Annotation Securing your Application Scan a Running Container Tracee Security Contexts for Pods Understanding Security Contexts AppArmor Profiles AppArmor Isolate Container Kernels gVisor Pod Security Pod Security Policies Deploy a PSP Pod Security Standards Enable PSS Open Policy Agent (OPA) Admission Controller Create a LimitRange Open Policy Agent Policy as Code Deploy Gatekeeper User Administration Contexts Contexts Authentication and Authorization Role Based Access Control Role Based Access Control RBAC Distributing Access Service Accounts Limit Pod Service Accounts Securing Secrets Secrets Create and Consume Secrets Hashicorp Vault Deploy Vault Securing the Network Networking Plugins NetworkPolicy Deploy a NetworkPolicy mTLS Linkerd mTLS with istio istio Threat Detection Active Threat Analysis Host Intrusion Detection Deploy OSSEC Network Intrusion Detection Deploy Suricata Physical Intrusion Detection Disaster Recovery Harsh Reality of Security Deploy a Response Plan Kasten K10 Backups Deploy K10

Duration 1 Days 6 CPD hours This course is intended for This course is designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks. Overview After completing this course, you will be able to: Provision an Azure Databricks workspace and cluster Use Azure Databricks to train a machine learning model Use MLflow to track experiments and manage machine learning models Integrate Azure Databricks with Azure Machine Learning Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this course, students will learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. Introduction to Azure Databricks Getting Started with Azure Databricks Working with Data in Azure Databricks Training and Evaluating Machine Learning Models Preparing Data for Machine Learning Training a Machine Learning Model Managing Experiments and Models Using MLflow to Track Experiments Managing Models Managing Experiments and Models Using MLflow to Track Experiments Managing Models Integrating Azure Databricks and Azure Machine Learning Tracking Experiments with Azure Machine Learning Deploying Models

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Unlock the power of AI with our ChatGPT Tutorial for Beginners. Learn to craft prompts, create content, and enhance your skills with AI text generation.

The course will show you how to build a website from scratch using WordPress.

Introduce kids and beginners to the world of coding with our Python Programming course. From setting up environments to creating projects, unlock the basics of Python in a fun and interactive way.

Learn to design, plan, and scale cloud implementations with Google Cloud Platform's BigQuery. This course will walk you through the fundamentals of applied machine learning and BigQuery ML along with its history, architecture, and use cases.

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Dive into the heart of Big Data Infrastructure, exploring storage systems, distributed file frameworks, and processing paradigms. This course provides a comprehensive understanding of key components like HDFS, Apache Spark, and Cassandra, offering insights into their architecture, use cases, and real-world applications. This course is a deep dive into the complex landscape of Big Data Infrastructure. From unravelling the architecture of Apache Spark to dissecting the benefits of distributed file systems, participants gain expertise in assessing, comparing, and implementing various Big Data storage and processing systems. Scalability, fault-tolerance, and industry-specific case studies add practical depth to theoretical knowledge. After the successful completion of this course, you will be able to: Understand the Components of Big Data Infrastructure, Including Storage Systems, Distributed File Systems, and Processing Frameworks. Identify the Characteristics and Benefits of Distributed File Systems Such as Hadoop Distributed File System (H.D.F.S). Describe the Architecture and Capabilities of Apache Spark and its Role in Big Data Processing. Recognise the Use Cases and Benefits of Apache Cassandra as a Distributed N..O.S.Q.L Database. Compare and Contrast Different Big Data Storage and Processing Systems Such as Hadoop, Spark, and Cassandra. Understand the Scalability and Fault-tolerance Mechanisms Used in Big Data Infrastructure, Such as Sharding and Replication. Appreciate the Challenges Associated with Deploying and Managing Big Data Infrastructure, Such as Hardware and Software Configuration and Security Considerations. Explore the intricacies of Big Data Infrastructure, from understanding storage systems to unraveling the nuances of distributed file frameworks and processing engines. Gain a comprehensive view of scalability, fault-tolerance mechanisms, and industry-specific challenges through engaging case studies. Equip yourself to navigate the dynamic landscape of Big Data with confidence and expertise. VIDEO - Course Structure and Assessment Guidelines Watch this video to gain further insight. Navigating the MSBM Study Portal Watch this video to gain further insight. Interacting with Lectures/Learning Components Watch this video to gain further insight. Big Data Infrastructure Self-paced pre-recorded learning content on this topic. Big Data Infrastructure Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be an added advantage to understanding the content of the course. The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone who is eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience. Big Data Infrastructure Engineer Hadoop Administrator Spark Developer Cassandra Database Administrator Big Data Solutions Architect Data Infrastructure Manager NoSQL Database Analyst Big Data Consultant Average Completion Time 2 Weeks Accreditation 3 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.

Learn the fundamentals of Blender software, explore techniques for creating detailed 3D models, and master the process of rendering your creations. Whether you're a beginner or an experienced designer, this course will equip you with the skills needed to bring your characters to life in the digital world.