42 Cloud courses in Liverpool

UNIX Virtualization and High Availability course description This course covers administering UNIX enterprise-wide with an emphasis on virtualization and high availability. What will you learn Manage Virtual Machines. Manage containers. Manage HA clusters. Manage HA cluster storage. UNIX Virtualization and High Availability course details Who will benefit: Enterprise-level UNIX professional. UNIX professionals working with virtualization and/or High availability. Prerequisites: Linux network administration 2 (LPIC-2) Duration 5 days UNIX Virtualization and High Availability course contents VIRTUALIZATION Virtualization concepts and theory Terminology, Pros and Cons of virtualization, variations of Virtual Machine monitors, migration of physical to VMs, migration of VMs between host systems, cloud computing. Xen Xen architecture, networking and storage, Xen configuration, Xen utilities, troubleshooting Xen installations, XAPI, XenStore, Xen Boot Parameters, the xm utility. KVM KVM architecture, networking and storage, KVM configuration, KVM utilities, troubleshooting KVM installations. Other virtualization solutions OpenVZ and LXC, other virtualization technologies, virtualization provisioning tools. Libvirt and Related Tools libvirt architecture, networking and storage, basic technical knowledge of libvirt and virsh, oVirt. Cloud Management Tools Basic feature knowledge of OpenStack and CloudStack, awareness of Eucalyptus and OpenNebula. Containers Containers versus VMs, Docker, Kubernetes. Load balanced clusters of LVS/IPVS, VRRP, configuration of keepalived, configuration of ldirectord, backend server network configuration. HAProxy, configuration of HAProxy. Failover clusters Pacemaker architecture and components (CIB, CRMd, PEngine, LRMd, DC, STONITHd), Pacemaker cluster configuration, Resource classes (OCF, LSB, Systemd, Upstart, Service, STONITH, Nagios), Resource rules and constraints (location, order, colocation), Advanced resource features (templates, groups, clone resources, multi-state resources), Pacemaker management using pcs, Pacemaker management using crmsh, configuration and management of corosync in conjunction with Pacemaker, other cluster engines (OpenAIS, Heartbeat, CMAN). HIGH AVAILABILITY CLUSTER STORAGE DRBD/cLVM DRBD resources, states and replication modes, configuration of DRBD resources, networking, disks and devices, configuration of DRBD automatic recovery and error handling, management of DRBD using drbdadm. drbdsetup and drbdmeta, Integration of DRBD with Pacemaker, cLVM, integration of cLVM with Pacemaker. Clustered File Systems Principles of cluster file systems. Create, maintain and troubleshoot GFS2 file systems in a cluster, create, maintain and troubleshoot OCFS2 file systems in a cluster, Integration of GFS2 and OCFS2 with Pacemaker, the O2CB cluster stack, other commonly used clustered file systems.

Securing Kubernetes training course description This course introduces concepts, procedures, and best practices to harden Kubernetes based systems and container-based applications against security threats. It deals with the main areas of cloud-native security: Kubernetes cluster setup, Kubernetes cluster hardening, hardening the underlying operating system and networks, minimizing microservices vulnerabilities, obtaining supply chain security as well as monitoring, logging, and runtime security. What will you learn Harden Kubernetes systems and clusters. Harden containers. Configure and use Kubernetes audit logs. Securing Kubernetes training course details Who will benefit: Technical staff working with Kubernetes Prerequisites: Kubernetes_for_engineers_course.htm Definitive Docker for engineers Duration 2 days Securing Kubernetes training course contents This course does not only deal with the daily security administration of Kubernetes-based systems but also prepares delegates for the official Certified Kubernetes Security Specialist (CKS) exams of the Cloud Native Computing Foundation (CNCF). Structure: 50% theory 50% hands on lab exercise Module 1: User and authorization management Users and service accounts in Kubernetes Authenticating users Managing authorizations with RBAC Module 2: Supply chain security Vulnerabilit checking for images Image validation in Kubernetes Reducing image footprint Secure image registries Module 3: Validating cluster setup and penetration testing Use CIS benchmark to review the security configuration of Kubernetes components. Modify the cluster components' configuration to match the CIS Benchmark. Penetration testing Kubernetes for known vulnerabilities. Module 4: System hardening Use kernel hardening tools Setup appropriate OS level security domains Container runtime sandboxes Limit network access Module 5: Monitoring and logging Configure Kubernetes audit logs Configure Audit Policies Monitor applications behaviour with Falco

Internet of Things training course description A concise overview course covering The Internet of Things and the technologies involved. Particular emphasis is placed on the high level architecture of IoT and the benefits achievable. What will you learn Describe the structure of the IoT List the technologies involved in IoT. Explain how IoT works. Internet of Things training course details Who will benefit: Non-technical staff working with IoT. Prerequisites: None. Duration 1 day Internet of Things training course contents What is IoT The Internet, what is IoT? IoT and M2M, IoT technologies, IoT architecture. Wired and wireless communication. IoT applications; Smart houses, smart cities, smart cars, wearable, environment, other domain specific IoTs. IoT architecture Physical objects, virtual objects, cloud computing, data capture, communications. Big data. Components Hardware, sensors, actuators, chips, firmware, embedded systems. Open source platforms. Power options: Battery, solar, PoE. IoT communication RF, ZigBee, Bluetooth, Bluetooth LE, RFID, WiFi, 802.11ah, mobile technologies. Wired. Arduino (as an example) Microcontrollers, the platform, development, Arduino software, reading from sensors, I2C, SPI. Arduino and the Internet, HTTP, WiFi, GSM. The cloud and IoT: Pachube, nimbits, ThingSpeak Security in IoT Authentication, Encryption, secure booting, firewalls.

Linux virtualization and HA training course description The LPIC-3 certification is the culmination of LPI's multi -level professional certification program. LPIC-3 is designed for the enterprise-level Linux professional and represents the highest level of professional, distribution neutral Linux certification within the industry. LPIC-3 304 covers administering Linux enterprise-wide with an emphasis on virtualization and high availability. At SNT we have enhanced the contents of the course by covering containers. What will you learn Manage Virtual Machines. Manage containers. Manage HA clusters. Manage HA cluster storage. Linux virtualization and HA training course details Who will benefit: Linux professionals working with virtualization and/or High availability. Prerequisites: Linux network administration 2 (LPIC-2) Duration 5 days Linux virtualization and HA training course contents VIRTUALIZATION Virtualization concepts and theory Terminology, Pros and Cons of virtualization, variations of Virtual Machine monitors, migration of physical to VMs, migration of VMs between host systems, cloud computing. Xen Xen architecture, networking and storage, Xen configuration, Xen utilities, troubleshooting Xen installations, XAPI, XenStore, Xen Boot Parameters, the xm utility. KVM KVM architecture, networking and storage, KVM configuration, KVM utilities, troubleshooting KVM installations. Other virtualization solutions OpenVZ and LXC, other virtualization technologies, virtualization provisioning tools. Libvirt and Related Tools libvirt architecture, networking and storage, basic technical knowledge of libvirt and virsh, oVirt. Cloud Management Tools Basic feature knowledge of OpenStack and CloudStack, awareness of Eucalyptus and OpenNebula. Containers Containers versus VMs, Docker, Kubernetes. Load balanced clusters of LVS/IPVS, VRRP, configuration of keepalived, configuration of ldirectord, backend server network configuration. HAProxy, configuration of HAProxy. Failover clusters Pacemaker architecture and components (CIB, CRMd, PEngine, LRMd, DC, STONITHd), Pacemaker cluster configuration, Resource classes (OCF, LSB, Systemd, Upstart, Service, STONITH, Nagios), Resource rules and constraints (location, order, colocation), Advanced resource features (templates, groups, clone resources, multi-state resources), Pacemaker management using pcs, Pacemaker management using crmsh, configuration and management of corosync in conjunction with Pacemaker, other cluster engines (OpenAIS, Heartbeat, CMAN). HIGH AVAILABILITY CLUSTER STORAGE DRBD/cLVM DRBD resources, states and replication modes, configuration of DRBD resources, networking, disks and devices, configuration of DRBD automatic recovery and error handling, management of DRBD using drbdadm. drbdsetup and drbdmeta, Integration of DRBD with Pacemaker, cLVM, integration of cLVM with Pacemaker. Clustered File Systems Principles of cluster file systems. Create, maintain and troubleshoot GFS2 file systems in a cluster, create, maintain and troubleshoot OCFS2 file systems in a cluster, Integration of GFS2 and OCFS2 with Pacemaker, the O2CB cluster stack, other commonly used clustered file systems.

Kubernetes for engineers training course description This course covers how Kubernetes addresses the challenges of distributed systems. Hands on sessions follow all the major theory chapters. What will you learn Explain what Kubernetes is and how it works. Create and run containers on Kubernetes using the Docker image format and container runtime. Kubernetes for engineers training course details Who will benefit: Anyone working with Docker or Kubernetes. Prerequisites: Definitive Docker for engineers. Duration 2 days Kubernetes for engineers training course contents Introduction Velocity, Scaling your service and your teams, Abstracting your infrastructure. Creating and running containers Container images, Building application images with Docker, Storing images in a remote registry, The Docker container runtime. Deploying a Kubernetes cluster Installing Kubernetes on a public cloud provider, Installing Kubernetes locally using minikube, Running Kubernetes on Raspberry Pi, The Kubernetes client, Cluster components. Common kubectl Commands Namespaces, Contexts, Viewing Kubernetes API objects, Creating, Updating, and Destroying Kubernetes objects, Labelling and annotating objects, Debugging commands. Pods Pods in Kubernetes, Thinking with pods, The pod manifest, Running pods, Accessing your pod, Health checks, Resource management, Persisting data with volumes, Putting It all together. Labels and Annotations Labels, Annotations. Service Discovery What Is Service discovery? The service object, Looking beyond the cluster, Cloud integration, Advanced details. ReplicaSets Reconciliation loops, Relating pods and ReplicaSets, Designing with ReplicaSets, ReplicaSet Spec, Creating a ReplicaSet, Inspecting a ReplicaSet, Scaling ReplicaSets, Deleting ReplicaSets. DaemonSets DaemonSet scheduler, Creating DaemonSets, Limiting DaemonSets to specific nodes, Updating a DaemonSet, Deleting a DaemonSet. Jobs The job object, Job patterns. ConfigMaps and secrets ConfigMaps, Secrets, Naming constraints, Managing ConfigMaps and secrets. Deployments Your first deployment, Creating deployments, Managing deployments, Updating deployments, Deployment strategies, Deleting a deployment. Integrating storage solutions and Kubernetes Importing external services, Running reliable singletons, Kubernetes-native storage with StatefulSets. Deploying real-world applications Parse, Ghost, Redis.

Complete C# programming training course description This training course teaches developers the programming skills that are required for developers to create Windows applications using the C# language. Students review the basics of C# program structure, language syntax, and implementation details, and then consolidate their knowledge throughout the week as they build an application that incorporates several features of the .NET Framework. What will you learn Use the syntax and features of C#. Create and call methods, catch and handle exceptions, and describe the monitoring requirements of large-scale applications. Implement a typical desktop application. Create class, define and implement interfaces, and create and generic collections. Read and write data to/from files. Build a GUI using XAML. Complete C# programming training course details Who will benefit: Programmers wishing to learn C#. Prerequisites: Developers attending this course should already have gained some limited experience using C# to complete basic programming tasks. Duration 5 days Complete C# programming training course contents Review of C# Syntax Overview of Writing Applications using C#, Datatypes, Operators, and Expressions. C# Programming Language Constructs. Hands on Developing the Class Enrolment Application. Methods, exceptions and monitoring apps Creating and Invoking Methods. Creating Overloaded Methods and Using Optional and Output Parameters. Handling Exceptions. Monitoring Applications. Hands on Extending the Class Enrolment Application Functionality. Developing a graphical application Implementing Structs and Enums. Organizing Data into Collections. Handling Events. Hands on Writing the Grades Prototype Application. Classes and Type-safe collections Creating Classes. Defining and Implementing Interfaces. Implementing Type-safe Collections. Hands on Adding Data Validation and Type-safety to the Grades Application. Class hierarchy using Inheritance Class hierarchies. Extending .NET framework classes. Creating generic types. Hands on Refactoring common functionality into the User Class. Reading and writing local data Reading and Writing Files. Serializing and Deserializing Data. Performing I/O Using Streams. Hands on Generating the Grades Report. Accessing a Database Creating and using entity data models. Querying and updating data by using LINQ. Hands on Retrieving and modifying grade data. Accessing remote data Accessing data across the web and in the cloud. Hands on Modifying grade data in the Cloud. Designing the UI for a graphical applicatione Using XAML to design a User Interface. Binding controls to data. Styling a UI. Hands on Customizing Student Photographs and Styling the Application. Improving performance and responsiveness Implementing Multitasking by using tasks and Lambda Expressions. Performing operations asynchronously. Synchronizing concurrent data access. Hands on Improving the responsiveness and performance of the application. Integrating with unmanaged code Creating and using dynamic objects. Managing the Lifetime of objects and controlling unmanaged resources. Hands on Upgrading the grades report. Creating reusable types and assemblies Examining Object Metadata. Creating and Using Custom Attributes. Generating Managed Code. Versioning, Signing and Deploying Assemblies. Hands on Specifying the Data to Include in the Grades Report. Encrypting and Decrypting Data Implementing Symmetric Encryption. Implementing Asymmetric Encryption. Hands on Encrypting and Decrypting Grades Reports.

About this training course Business Impact: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets. Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on 2-day training course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. Unique Features: Eight business use cases covering their business impact, code walkthroughs for most all and solution approach. Industry data sets for participants to practice on and take home. No software or complicated Python frameworks required. Training Objectives After the completion of this training course, participants will be able to: Understand digital oil field transformation and its impact on business Examine machine learning methods Review workflows and code implementations After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models Target Audience This training course is suitable and will greatly benefit the following specific groups: Artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve the facility with different data analysis techniques applied on large data sets Course Level Intermediate Advanced Training Methods The course discusses several business use-cases that are amenable to data-driven workflows. For each use case, the instructor will show the solution using a data analysis technique with Python code deployed in the Google cloud. Trainees will solve a problem and tweak their solution. Course Duration: 2 days in total (14 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts Knowledge of Python is not a must but preferred to get the full benefit. The training will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free) Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

Ansible for engineers training course description An introduction to automation using ansible. Ansible is a general purpose IT automation platform that can be use for a number of purposes. The course covers configuration management, cloud provisioning and application deployment with ansible. Hands on sessions follow all major sections. What will you learn Install ansible. Automate tasks with ansible. Write ansible playbooks. Ansible for engineers training course details Who will benefit: Administrators and developers automating tasks. Prerequisites: Linux administration skills Duration 3 days Ansible for engineers training course contents What is ansible? The language, the engine, the framework. Uses of ansible, orchestration. Hands on Installing ansible. Ansible architecture ible architecture Controlling machines, nodes, Agentless, SSH, modules, JSON protocol. Configuration management, inventories, playbooks, modules, roles. Hands on Getting started, running ad hoc commands. Ansible and Vagrant Prototyping and testing. Hands on Using ansible with Vagrant. Ad hoc commands Parallelism, shell commands, managing files and directories, file transfer, package management, manage user and groups, deploying applications, service management, background jobs, checking log files, managing cron jobs. Hands on Using ansible with Vagrant. Playbooks ansible-playbook, users, sudo, YAML, plays, tasks, handlers, modules. Hands on Running playbooks. More playbooks Handlers, variables, environmental variables, playbook variables, inventory variables, variable scope and precedence, accessing variables, facts, ansible vault. Conditionals, wait_for. Hands on Using variables and conditions in playbooks. Roles and includes Dynamic includes, Handler includes, playbook includes. Roles, role parts: handlers, files, templates, cross platform roles, ansible galaxy. Hands on includes example, building roles. Inventories /etc/ansible/hosts, inventory variables, static inventories, dynamic inventories. Hands on Inventories and variables. Miscellanea Individual server cookbooks, Main playbook for configuring all servers. Hands onPlaybooks.

OpenStack for NFV and SDN course description OpenStack is predominately a cloud management technology. This course looks at how OpenStack can be used in a NFV and SDN environment. What will you learn Describe the architecture of NFV. Explain the relationship between NFV and SDN. Implement NFV VIM using OpenStack. Explain how OpenStack as VNFM and orchestrator works. OpenStack for NFV and SDN course details Who will benefit: Anyone wishing to implement NFV using OpenStack. Prerequisites: Introduction to Virtualization Duration 3 day OpenStack for NFV and SDN course content What is NFV? What is NFV? What are network Functions? NFV benefits, NFV market drivers. ETSI NFV framework. ETSI documents, Architecture overview, compute domain, hypervisor domain, infrastructure network domain. What is OpenStack? Virtual machines, clouds, management. OpenStack architecture, OpenStack modules. Why OpenStack for NFV? Hands on OpenStack installation. OpenStack Virtualization and NFV Server, storage and network virtualization and NFV. Where OpenStack fits in the ETSI framework. Virtual machines, containers and docker. Data centres, clouds, SaaS, IaaS, PaaS. Hands on OpenStack Iaas, OpenStack Nova. The virtualization layer VM centric model, containers versus hypervisors, FD.io. Hands on OpenStack as the VIM. OpenStack Neutron VXLAN, Networks, subnets, ports. Security groups. Routers. Service and component hierarchy. Hands on Implementing a virtual network with OpenStack Neutron. Virtualization of Network Functions Network virtualization versus Network Function virtualization. NFV MANO Management and Orchestration. Where OpenStack fits. MANO descriptors, Open orchestration. OpenStack Tacker, Open MANO, OpenBaton, other orchestrators. OpenStack Tacker Installation, getting started, configuration. SFC and OpenStack. Hands on Deploying a VNF. OPNFV What is OPNFV, Where OpenStack fits into OPNFV. SDN What is SDN? Control and data planes. SDN controllers. Classic SDN versus real SDN. Hybrid SDN, network automation, SDN with overlays. Northbound, southbound, SDN protocols, OpenFlow, OpenDaylight, ONOS, SDN with NFV. SDN and OpenStack. Summary Deploying NFV, performance, testing. Futures

Network forensics training course description This course studies network forensics-monitoring and analysis of network traffic for information gathering, intrusion detection and legal evidence. We focus on the technical aspects of network forensics rather than other skills such as incident response procedures etc.. Hands on sessions follow all the major sections. What will you learn Recognise network forensic data sources. Perform network forensics using: Wireshark NetFlow Log analysis Describe issues such as encryption. Network forensics training course details Who will benefit: Technical network and/or security staff. Prerequisites: TCP/IP foundation for engineers. Duration 3 days Network forensics training course contents What is network forensics? What it is, host vs network forensics, purposes, legal implications, network devices, network data sources, investigation tools. Hands on whois, DNS queries. Host side network forensics Services, connections tools. Hands on Windows services, Linux daemons, netstat, ifoconfig/ipconfig, ps and Process explorer, ntop, arp, resource monitor. Packet capture and analysis Network forensics with Wireshark, Taps, NetworkMiner. Hands on Performing Network Traffic Analysis using NetworkMiner and Wireshark. Attacks DOS attacks, SYN floods, vulnerability exploits, ARP and DNS poisoning, application attacks, DNS ANY requests, buffer overflow attacks, SQL injection attack, attack evasion with fragmentation. Hands on Detecting scans, using nmap, identifying attack tools. Calculating location Timezones, whois, traceroute, geolocation. Wifi positioning. Hands on Wireshark with GeoIP lookup. Data collection NetFlow, sflow, logging, splunk, splunk patterns, GRR. HTTP proxies. Hands on NetFlow configuration, NetFlow analysis. The role of IDS, firewalls and logs Host based vs network based, IDS detection styles, IDS architectures, alerting. Snort. syslog-ng. Microsoft log parser. Hands on syslog, Windows Event viewer. Correlation Time synchronisation, capture times, log aggregation and management, timelines. Hands on Wireshark conversations. Other considerations Tunnelling, encryption, cloud computing, TOR. Hands on TLS handshake in Wireshark.