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Introduction When it comes to academic writing, referencing is crucial. It’s not just about giving credit where it’s due, but also about enhancing the credibility of your work. Among the various referencing styles, Harvard referencing for word essays is one of the most widely used, especially in the UK. But why is it so popular, and how can you master it? Let’s dive in! Understanding Harvard Referencing Harvard referencing is a system of citation that allows you to acknowledge the sources you’ve used in your academic writing. It’s an author-date system, which means you include the author’s surname and the year of publication within the text, and provide full details in a reference list at the end. Despite its name, Harvard references didn’t originate from Harvard University. The style was first used by a zoologist named Edward Laurens Mark in a paper published in 1881. Over time, it has evolved and become one of the most recognized citation styles worldwide. Harvard referencing consists of two main components: in-text citations and a reference list. In-text citations are brief and are placed within the body of your text, while the reference list provides full details of all the sources you’ve cited. The Basics of Harvard Referencing In-text citations in Harvard referencing include the author’s surname and the year of publication. If you’re quoting directly, you should also include the page number. For example: (Smith, 2020, p. 23). The reference list is where you provide full details of each source cited in your work. Each entry should include the author’s name, year of publication, title of the work, and other relevant information, depending on the type of source. A reference list includes only the sources you’ve directly cited in your text, while a bibliography may include all sources you’ve consulted, whether or not you’ve cited them. How to Cite Different Types of Sources When citing a book, you should include the author’s name, year of publication, title (in italics), edition (if applicable), place of publication, and publisher. For example: Smith, J. (2020). The Art of Referencing (2nd ed.). London: Academic Press. Journal articles should include the author’s name, year of publication, title of the article, title of the journal (in italics), volume number, issue number, and page numbers. For example: Brown, A. (2019). ‘Understanding Harvard Referencing’, Journal of Academic Writing, 12(3), pp. 45-67. For websites, include the author (or organization), year of publication (or last update), title of the webpage (in italics), and the URL. For example: University of Oxford. (2021). Harvard Referencing Guide. Available at: https://www.ox.ac.uk/harvard (Accessed: 10 September 2024). When citing a newspaper article, include the author’s name, year of publication, title of the article, title of the newspaper (in italics), date, and page number. For example: Doe, J. (2022). ‘The Future of Academic Writing’, The Times, 5 May, p. 12. For government documents, include the name of the government department, year of publication, title of the document (in italics), place of publication, and publisher. For example: UK Department for Education. (2020). Education Policy Report. London: HMSO. Cite conference papers by including the author’s name, year of publication, title of the paper, title of the conference (in italics), location, and date of the conference. For example: Jones, M. (2023). ‘Innovations in Academic Writing’, International Writing Conference, Manchester, 10-12 April. Harvard Referencing for Electronic Sources When citing e-books, the format is similar to print books, but you should include the format (e.g., Kindle) or the platform where it’s available. For example: Adams, R. (2018). Digital Referencing (Kindle ed.). Available at: Amazon.co.uk. For online journals, follow the same format as print journals, but include the DOI or the URL where the article can be accessed. For example: Taylor, S. (2021). ‘Harvard Referencing in the Digital Age’, Journal of Modern Research, 15(4), pp. 33-50. DOI: 10.1234/jmr.2021.0987. The format for websites remains the same as mentioned earlier, with the inclusion of the URL and the access date. For social media, include the author (or username), year, content of the post (in italics), platform, and the date of the post. For example: @AcademicWriterUK. (2023). The key to successful referencing is consistency. Twitter, 15 March. Advanced Harvard Referencing Techniques When you can’t access the original source and need to cite a secondary source, mention both the original and secondary source. For example: (Smith, 1989, cited in Brown, 2019). For works with multiple authors, list up to three authors in the in-text citation. For more than three, use “et al.” after the first author’s name. For example: (Jones, Smith, and Brown, 2020) or (Jones et al., 2020). If you’re citing different editions of a book, mention the edition in your reference. For example: Brown, A. (2018). Theories of Learning (3rd ed.). Oxford: Oxford University Press. If some information is missing, like the author or date, use “Anon.” for anonymous authors and “n.d.” for no date. Common Harvard Referencing Mistakes and How to Avoid Them Incorrect formatting can easily occur if you’re not careful. Make sure to follow the correct structure for each type of source, as this can significantly impact your grade. Leaving out essential details like the author’s name or the year of publication is a common mistake. Always double-check your references before submitting your work. Punctuation is crucial in Harvard referencing. Misplacing a comma or period can change the meaning of your citation. Pay attention to the details! Tools and Resources for Harvard Referencing Online citation generators can save you time, but be cautious—always double-check the generated citations for accuracy. Tools like EndNote or Zotero can help you manage your references and ensure consistency throughout your work. Many universities provide specific guidelines for Harvard referencing. Always refer to your institution’s guidelines to ensure you’re following the correct format. The Importance of Consistency in Harvard Referencing Consistency is key in Harvard referencing. Uniformity in your citations not only makes your work look professional but also enhances readability. Inconsistent referencing can confuse readers and may lead to a lower grade. Make sure your citations follow a consistent style throughout your work. Tips for Mastering Harvard Referencing Always double-check your references before submission. Small errors can add up and impact your overall grade. Keep a record of all the sources you consult. This will make it easier to create your reference list and avoid missing any citations. Using a Harvard style guide can help ensure that you’re following the correct format. It’s a handy tool for quick reference. Conclusion Mastering Harvard referencing may seem daunting at first, but with practice and attention to detail, it becomes second nature. Remember, proper referencing not only supports your arguments but also adds credibility to your work. So, take your time, be consistent, and use the resources available to you.

Duration 3 Days 18 CPD hours This course is intended for This class is intended for the following customer job roles: Cloud architects, administrators, and SysOps personnel Cloud developers and DevOps personnel Overview This course teaches participants the following skills: Plan and implement a well-architected logging and monitoring infrastructure Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs) Create effective monitoring dashboards and alerts Monitor, troubleshoot, and improve Google Cloud infrastructure Analyze and export Google Cloud audit logs Find production code defects, identify bottlenecks, and improve performance Optimize monitoring costs This course teaches you techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud. Guided by the principles of Site Reliability Engineering (SRE), and using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage. Introduction to Google Cloud Monitoring Tools Understand the purpose and capabilities of Google Cloud operations-focused components: Logging, Monitoring, Error Reporting, and Service Monitoring Understand the purpose and capabilities of Google Cloud application performance management focused components: Debugger, Trace, and Profiler Avoiding Customer Pain Construct a monitoring base on the four golden signals: latency, traffic, errors, and saturation Measure customer pain with SLIs Define critical performance measures Create and use SLOs and SLAs Achieve developer and operation harmony with error budgets Alerting Policies Develop alerting strategies Define alerting policies Add notification channels Identify types of alerts and common uses for each Construct and alert on resource groups Manage alerting policies programmatically Monitoring Critical Systems Choose best practice monitoring project architectures Differentiate Cloud IAM roles for monitoring Use the default dashboards appropriately Build custom dashboards to show resource consumption and application load Define uptime checks to track aliveness and latency Configuring Google Cloud Services for Observability Integrate logging and monitoring agents into Compute Engine VMs and images Enable and utilize Kubernetes Monitoring Extend and clarify Kubernetes monitoring with Prometheus Expose custom metrics through code, and with the help of OpenCensus Advanced Logging and Analysis Identify and choose among resource tagging approaches Define log sinks (inclusion filters) and exclusion filters Create metrics based on logs Define custom metrics Link application errors to Logging using Error Reporting Export logs to BigQuery Monitoring Network Security and Audit Logs Collect and analyze VPC Flow logs and Firewall Rules logs Enable and monitor Packet Mirroring Explain the capabilities of Network Intelligence Center Use Admin Activity audit logs to track changes to the configuration or metadata of resources Use Data Access audit logs to track accesses or changes to user-provided resource data Use System Event audit logs to track GCP administrative actions Managing Incidents Define incident management roles and communication channels Mitigate incident impact Troubleshoot root causes Resolve incidents Document incidents in a post-mortem process Investigating Application Performance Issues Debug production code to correct code defects Trace latency through layers of service interaction to eliminate performance bottlenecks Profile and identify resource-intensive functions in an application Optimizing the Costs of Monitoring Analyze resource utilization cust for monitoring related components within Google Cloud Implement best practices for controlling the cost of monitoring within Google Cloud

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: IT/Senior managers Solutions architects/Enterprise architects Operations professionals Overview This course teaches you how to: Build your cloud strategy. Develop the hiring plan for your cloud team. Choose and prioritize which applications to move to AWS. Build a migration plan for moving workloads to AWS. Manage your AWS expenditures and internal chargebacks. This course teaches you how to select the right strategy, people, migration plan, and financial management methodology needed when moving your workloads to the cloud. This course provides guidance on how to build a holistic cloud adoption plan and how to hire people who will execute that plan. You will learn best practices for choosing workloads to migrate from your on-premises environment to AWS. In addition, you will also learn best practices for managing your AWS expenses and dealing with internal chargebacks. Building Your Cloud StrategyHiring Your Cloud TeamMigration PlanningCloud Expenditure Management

Duration 3 Days 18 CPD hours This course is intended for The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services. Overview You will be able to Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description Explain the Benefits of Artificial Intelligence (AI) Describe how we Learn from Data - Functionality, Software and Hardware Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together Describe a ''Learning from Experience'' Agile Approach to Projects Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI) Ethical and Sustainable Human and Artificial Intelligence (AI) Recall the General Definition of Human and Artificial Intelligence (AI) Describe what are Ethics and Trustworthy Artificial Intelligence (AI) Describe the Three Fundamental Areas of Sustainability and the United Nationïs Seventeen Sustainability Goals Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI) Artificial Intelligence (AI) and Robotics Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description Describe what a Robot is Describe what an intelligent Robot is Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations Explain the Benefits of Artifical Intelligence (AI) Describe the Challenges of Artificial Intelligence (AI) Projects Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects List Opportunities for Artificial Intelligence (AI) Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs) Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice Describe how we Learn from Data - Functionality, Software and Hardware Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsïFunctionality The Management, Roles and Responsibilities of Humans and Machines Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together List Future Directions of Humans and Machines Working Together Describe a ''Learning from Experience'' Agile Approach to Projects

Duration 5 Days 30 CPD hours This course is intended for Network and security architects and consultants who design the enterprise and data center networks and VMware NSX environments Overview By the end of the course, you should be able to meet the following objectives: Describe and apply a design framework Apply a design process for gathering requirements, constraints, assumptions, and risks Design a VMware vSphere virtual data center to support NSX-T Data Center requirements Create a VMware NSX Manager™ cluster design Create a VMware NSX Edge™ cluster design to support traffic and service requirements in NSX-T Data Center Design logical switching and routing Recognize NSX-T Data Center security best practices Design logical network services Design a physical network to support network virtualization in a software-defined data center Create a design to support the NSX-T Data Center infrastructure across multiple sites Describe the factors that drive performance in NSX-T Data Center This five-day course provides comprehensive training on considerations and practices to design a VMware NSX-T? Data Center environment as part of a software-defined data center strategy. This course prepares the student with the skills to lead the design of NSX-T Data Center offered in release 3.2, including design principles, processes, and frameworks. The student gains a deeper understanding of the NSX-T Data Center architecture and how it can be used to create solutions to address the customer?s business needs. Course Introduction Introduction and course logistics Course objectives Design Concepts Identify design terms Describe framework and project methodology Describe VMware Validated Design? Identify customers? requirements, assumptions, constraints, and risks Explain the conceptual design Explain the logical design Explain the physical design NSX Architecture and Components Recognize the main elements in the NSX-T Data Center architecture Describe the NSX management cluster and the management plane Identify the functions and components of management, control, and data planes Describe the NSX Manager sizing options Recognize the justification and implication of NSX manager cluster design decisions Identify the NSX management cluster design options NSX Edge Design Explain the leading practices for edge design Describe the NSX Edge VM reference designs Describe the bare-metal NSX Edge reference designs Explain the leading practices for edge cluster design Explain the effect of stateful services placement Explain the growth patterns for edge clusters Identify design considerations when using L2 bridging services NSX Logical Switching Design Describe concepts and terminology in logical switching Identify segment and transport zone design considerations Identify virtual switch design considerations Identify uplink profile, VMware vSphere© Network I/O Control profile, and transport node profile design considerations Identify Geneve tunneling design considerations Identify BUM replication mode design considerations NSX Logical Routing Design Explain the function and features of logical routing Describe NSX-T Data Center single-tier and multitier routing architectures Identify guidelines when selecting a routing topology Describe the BGP and OSPF routing protocol configuration options Explain gateway high availability modes of operation and failure detection mechanisms Identify how multitier architectures provide control over stateful service location Identify VRF Lite requirements and considerations Identify the typical NSX scalable architectures NSX Security Design Identify different security features available in NSX-T Data Center Describe the advantages of an NSX Distributed Firewall Describe the use of NSX Gateway Firewall as a perimeter firewall and as an intertenant firewall Determine a security policy methodology Recognize the NSX-T Data Center security best practices NSX Network Services Identify the stateful services available in different edge cluster high availability modes Describe failover detection mechanisms Explain the design considerations for integrating VMware NSX© Advanced Load Balancer? with NSX-T Data Center Describe stateful and stateless NSX-T Data Center NAT Identify benefits of NSX-T Data Center DHCP Identify benefits of metadata proxy Describe IPSec VPN and L2 VPN Physical Infrastructure Design Identify the components of a switch fabric design Assess Layer 2 and Layer 3 switch fabric design implications Review guidelines when designing top-of-rack switches Review options for connecting transport hosts to the switch fabric Describe typical designs for VMware ESXi? compute hypervisors with two pNICs Describe typical designs for ESXi compute hypervisors with four or more pNICs Describe a typical design for a KVM compute hypervisor with two pNICs Differentiate dedicated and collapsed cluster approaches to SDDC design NSX Multilocation Design Explain scale considerations in an NSX-T Data Center multisite design Describe the main components of the NSX Federation architecture Describe the stretched networking capability in Federation Describe stretched security use cases in Federation Compare Federation disaster recovery designs NSX Optimization Describe Geneve Offload Describe the benefits of Receive Side Scaling and Geneve Rx Filters Explain the benefits of SSL Offload Describe the effect of Multi-TEP, MTU size, and NIC speed on throughput Explain the available N-VDS enhanced datapath modes and use cases List the key performance factors for compute nodes and NSX Edge nodes

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Individuals planning to deploy applications and create application environments on Google Cloud Platform Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs. Overview This course teaches participants the following skills: Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing,storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences This 1-day instructor led course introduces Azure professionals to the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, Solution Architects and SysOps Administrators familiar with Azure features and setup; and want to gain experience configuring Google Cloud products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. Introducing Google Cloud Explain the advantages of Google Cloud. Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Getting Started with Google Cloud Identify the purpose of projects on Google Cloud. Understand how Azure's resource hierarchy differs from Google Cloud's Understand the purpose of and use cases for Identity and Access Management. Understand how Azure AD differs from Google Cloud IAM. List the methods of interacting with Google Cloud. Launch a solution using Cloud Marketplace. Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine Understand the basics of networking in Google Cloud. Understand how Azure VPC differs from Google VPC. Understand the similarities and differences between Azure VM and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in Azure. Deploy applications using Google Compute Engine Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Azure Blob compares to Cloud Storage. Compare Google Cloud?s managed database services with Azure SQL. Learn how to choose among the various storage options on Google Cloud. Load data from Cloud Storage into BigQuery Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Azure Kubernetes Service differs from from Google Kubernetes Engine. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Azure App Service. Understand the purpose of and use cases for Google Cloud Endpoints. Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Google Cloud Deployment Manager differs from Azure Resource Manager. Understand the purpose of integrated monitoring, alerting, and debugging Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from Azure Data Lake. Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus. Understand how Google Cloud?s machine-learning APIs differ from Azure's. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data Summary and Review Review the products that make up Google Cloud and remember how to choose among them Understand next steps for training and certification Understand, at a high level, the process of migrating from Azure to Google Cloud.

Duration 1 Days 6 CPD hours This course is intended for Individuals planning to deploy applications and create application environments on Google Cloud. Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview Identify the purpose and value of Google Cloud products and services. Interact with Google Cloud services. Describe ways in which customers have used Google Cloud. Choose among and use application deployment environments on Google Cloud: App Engine, Google Kubernetes Engine, and Compute Engine. Choose among and use Google Cloud storage options: Cloud Storage, Cloud SQL, Cloud Bigtable, and Firestore. Make basic use of BigQuery, Google's managed data warehouse for analytics. This course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies. Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform. Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Getting Started with Google Cloud Platform Identify the purpose of projects on Google Cloud Platform. Understand the purpose of and use cases for Identity and Access Management. List the methods of interacting with Google Cloud Platform. Lab: Getting Started with Google Cloud Platform. Google Compute Engine and Networking Identify the purpose of and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Lab: Deploying Applications Using Google Compute Engine. Google Cloud Platform Storage Options Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable. Learn how to choose between the various storage options on Google Cloud Platform. Lab: Integrating Applications with Google Cloud Storage. Google Container Engine Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Introduction to Hybrid and Multi-Cloud computing (Anthos). Lab: Deploying Applications Using Google Container Engine. Google App Engine and Google Cloud Datastore Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand the purpose of and use cases for Google Cloud Endpoints. Lab: Deploying Applications Using App Engine and Cloud Datastore. Deployment and Monitoring Understand the purpose of template-based creation and management of resources. Understand the purpose of integrated monitoring, alerting, and debugging. Lab: Getting Started with Stackdriver and Deployment Manager. Big Data and Machine Learning Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Lab: Getting Started with BigQuery. Summary and Review Summary and Review. What's Next?.

Duration 2 Days 12 CPD hours This course is intended for This course is intended for system and network administrators or operators responsible for the installation, setup, configuration, and administration of the BIG-IQ system or management of BIG-IP devices and F5 products running on those devices. This course uses lectures and hands-on exercises to give participants real-time experience in configuring and using the BIG-IQ© product. Students are introduced to BIG-IQ, its interface, and its various functionality. We first look at administering and operating the BIG-IQ system itself, then detail how it is used to remotely manage BIG-IP devices running BIG-IP Local Traffic Manager (LTM). We then look configuring a BIG-IQ Data Collection Device (DCD) system and see how it is used for the remote storage and examination of BIG-IP log events and statistics. Module 1: BIG-IQ Overview BIG-IQ Centralized Management BIG-IQ Components BIG-IQ Core Functionality REST API BIG-IQ Data Collection Device (DCD) BIG-IP Cloud Edition (CE) Setting up the BIG-IQ System Module 2: Administering the BIG-IQ System Controlling Access to the BIG-IQ Creating, Authenticating, Configuring Users Backups Local Host Settings Configuring DNS, NTP, and SMTP Monitoring BIG-IQ, DCD, and BIG-IP Events with Alerts Monitoring BIG-IQ with iHealth Post Installation Issues; Licensing, Changing Management IP, Master Key, Restoring Backups Module 3: Managing BIG-IP LTM Devices BIG-IP LTM Device Discovery BIG-IP Device Backup Deploying to BIG-IP Devices Deployment and Deployment Logs Configuration Snapshots Managing BIG-IP Certificates Managing BIG-IP Licenses Monitoring BIG-IP Devices with iHealth Management of QKView Reports from Managed BIG-IP Devices Module 4: Setting Up the BIG-IQ Data Collection Device Custom Roles Types and Groups Setting up User Accounts with custom roles and privileges Managing BIG-IP DSC Discovery and management of BIG-IP Device Clusters (DSC) with BIG-IQ Administering BIG-IQ High Availability Configuration and management of BIG-IQ systems in a High Availability pair

Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources Additional course details: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Google Cloud Platform Big Data and Machine Learning Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for Cloud Solutions Architects, DevOps Engineers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with a focus on Google Compute Engine. Overview Configure VPC networks and virtual machines Administer Identity and Access Management for resources Implement data storage services in GCP Manage and examine billing of GCP resources Monitor resources using Stackdriver services Connect your infrastructure to GCP Configure load balancers and autoscaling for VM instances Automate the deployment of GCP infrastructure services Leverage managed services in GCP This class introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform, with a focus on Compute Engine. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems, and application services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. Introduction to Google Cloud Platform List the different ways of interacting with GCP Use the GCP Console and Cloud Shell Create Cloud Storage buckets Use the GCP Marketplace to deploy solutions Virtual Networks List the VPC objects in GCP Differentiate between the different types of VPC networks Implement VPC networks and firewall rules Design a maintenance server Virtual Machines Recall the CPU and memory options for virtual machines Describe the disk options for virtual machines Explain VM pricing and discounts Use Compute Engine to create and customize VM instances Cloud IAM Describe the Cloud IAM resource hierarchy Explain the different types of IAM roles Recall the different types of IAM members Implement access control for resources using Cloud IAM Storage and Database Services Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable Choose a data storage service based on your requirements Implement data storage services Resource Management Describe the cloud resource manager hierarchy Recognize how quotas protect GCP customers Use labels to organize resources Explain the behavior of budget alerts in GCP Examine billing data with BigQuery Resource Monitoring Describe the Stackdriver services for monitoring, logging, error reporting, tracing, and debugging Create charts, alerts, and uptime checks for resources with Stackdriver Monitoring Use Stackdriver Debugger to identify and fix errors Interconnecting Networks Recall the GCP interconnect and peering services available to connect your infrastructure to GCP Determine which GCP interconnect or peering service to use in specific circumstances Create and configure VPN gateways Recall when to use Shared VPC and when to use VPC Network Peering Load Balancing and Autoscaling Recall the various load balancing services Determine which GCP load balancer to use in specific circumstances Describe autoscaling behavior Configure load balancers and autoscaling Infrastructure Automation Automate the deployment of GCP services using Deployment Manager or Terraform Outline the GCP Marketplace Managed Services Describe the managed services for data processing in GCP Additional course details: Nexus Humans Architecting with Google Compute Engine training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Architecting with Google Compute Engine course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.