232 Courses in Cardiff delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for There is no specific prerequisite for the CDRP© course. However, participants who have at least three years' experience in a data centre and/or IT infrastructures will be best suited. Overview After completion of the course, the participant will be able to: 1. Understand the different standards and methodologies for risk management and assessment 2. Establish the required project team for risk management 3. Perform the risk assessment, identifying current threats, vulnerabilities and the potential impact based on customised threat catalogues 4. Report on the current risk level of the data centre both quantitative and qualitative 5. Anticipate and minimise potential financial impacts 6. Understand the options for handling risk 7. Continuously monitor and review the status of risk present in the data centre 8. Reduce the frequency and magnitude of incidents 9. Detect and respond to events when they occur 10. Meet regulatory and compliance requirements 11. Support certification processes such as ISO/IEC 27001 12. Support overall corporate and IT governance Introduction to Risk Management Risk management concepts Senior management and risk Enterprise Risk Management (ERM) Benefits of risk management Data Centre Risk and Impact Risk in facility, power, cooling, fire suppression, infrastructure and IT services Impact of data centre downtime Main causes of downtime Cost factors in downtime Standards, Guidelines and Methodologies ISO/IEC 27001:2013, ISO/IEC 27005:2011, ISO/IEC 27002:2013 NIST SP 800-30 ISO/IEC 31000:2009 SS507:2008 ANSI/TIA-942 Other methodologies (CRAMM, EBIOS, OCTAVE, etc.) Risk Management Definitions Asset Availability/Confidentiality/Integrity Control Information processing facility Information security Policy Risk Risk analysis/Risk assessment/Risk evaluation/ Risk treatment Threat/Vulnerability Types of risk Risk Assessment Software The need for software Automation Considerations Risk Management Process The risk management process Establishing the context Identification Analysis Evaluation Treatment Communication and consultation Monitoring and review Project Approach Project management principles Project management methods Scope Time Cost Cost estimate methods Context Establishment General considerations Risk evaluation, impact and acceptance criteria Severity rating of impact Occurrence rating of probability Scope and boundaries Scope constraints Roles & responsibilities Training, awareness and competence Risk Assessment - Identification The risk assessment process Identification of assets Identification of threats Identification of existing controls Identification of vulnerabilities Identification of consequences Hands-on exercise: Identification of assets, threats, existing controls, vulnerabilities and consequences Risk Assessment - Analysis and Evaluation Risk estimation Risk estimation methodologies Assessment of consequences Assessment of incident likelihood Level of risk estimation Risk evaluation Hands-on exercise: Assessment of consequences, probability and estimating level of risk Risk Treatment The risk treatment process steps Risk Treatment Plan (RTP) Risk modification Risk retention Risk avoidance Risk sharing Constraints in risk modification Control categories Control examples Cost-benefit analysis Control implementation Residual risk Communication Effective communication of risk management activities Benefits and concerns of communication Risk Monitoring and Review Ongoing monitoring and review Criteria for review Risk scenarios Risk assessment approach Data centre site selection Data centre facility Cloud computing UPS scenarios Force majeure Organisational shortcomings Human failure Technical failure Deliberate acts Exam: Certified Data Centre Risk Professional Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Duration 2 Days 12 CPD hours This course is intended for A prior understanding of EU Data Protection legislation is recommended. Candidates are typically management professionals and decision-makers who already have responsibility for data protection compliance within their organisation.Co-Requisite Subjects Candidates should have a good understanding of their own organisation?s data management activities through the life cycle from initial acquisition, through the various areas of processing and usage, to eventual removal or destruction. Overview To equip the learner with a foundational understanding of the principles of the General Data Protection Regulation (GDPR) and to provide constructive suggestions on implementing compliant processes. The social, historical and legal background leading to the General Data Protection Regulation (GDPR) The scope and global context of the GDPR The key concepts within the GDPR The definition of all key words and phrases relating to this Data Protection regulation Principle One: The criteria governing fair, open and transparent processing of personal data Principle Two: Purpose Limitation, the challenge of limiting the processing within the context of specified and lawful purposes Principle Three: Minimisation of processing, and ensuring that only that data is processed which is necessary to achieve the purpose. Principle Two: Purpose Limitation, the challenge of limiting the processing within the context of specified and lawful purposes Principle Three: Minimisation of processing, and ensuring that only that data is processed which is necessary to achieve the purpose. Principle Four: Ensuring that any personal data held by the organisation is kept accurate and current, and that any processing of such data is appropriate Principle Five: Management and storage of personal data in a manner that meets regulatory obligations, while minimising the time that the individual remains identifiable Principle Six: The criteria governing safe, secure and confidential processing of personal data in order to protect its integrity Principle Seven: The key roles, responsibilities and accountabilities of those involved in Data Management within an organisation Establishment within a single Member State Joint Controllers Privacy by Design and by Default Nominated Representatives Third-party Contracts and shared liability Logging of data management processes Data Breach Notification obligations Privacy Impact Assessments Overseas transfer of personal data L2.8 The Data Subject Rights, and their implications for the Data Controller and the Data Processor L2.8.1 The ?right to be forgotten? L2.8.2 The right to restriction of processing L2.8.3 The right to object to certain processing L2.8.4 The right to have inaccurate data amended or erased L2.8.5 The right to data portability L2.8.6 The right of access to one?s personal data L2.8.7 Rights in relation to automated decision-making and profiling The role of the Data Protection Officer (DPO) The role of the Data Protection Officer (DPO) Criteria for designating a DPO Tasks of the DPO Position of the DPO within the organisation The role of the Supervisory Authority within the Member State The Lead Supervisory Authority and independence Investigative, corrective and advisory powers Independence of the Supervisory Authority Collaboration with other Supervisory Authorities Codes of Conduct and Certification The role, powers and tasks of the European Data Protection Board (EDPB) The remedies, liabilities and penalties available under the GDPR Right to raise a complaint Right to representation Right to effective judicial remedy Right to compensation and liability Administrative fines of up to ?10m or 2% of GAT Administrative fines of up to ?20m or 4% of GAT Provisions for specific processing situations Freedom of Expression Processing of official documents Processing of National Identification Numbers Processing regarding employment Processing for archiving purposes Processing under obligations of official secrecy Processing of data by religious organisations Preparing for implementation of the GDPR Review of data management policies and procedures Review of data assets and security structures Training and Awareness-raising Data management governance structures Embedding Privacy By Design and Default Codes of Conduct and Certification against standards Breach detection and notification procedures Review of third-party agreements, contracts

Duration 3 Days 18 CPD hours This course is intended for This course is for Azure Administrators who migrate and manage SAP solutions on Azure. Azure Administrators manage the cloud services that span storage, networking, and compute cloud capabilities, with a deep understanding of each service across the full IT lifecycle. They take end-user requests for new cloud applications and make recommendations on services to use for optimal performance and scale, as well as provision, size, monitor and adjust as appropriate. This role requires communicating and coordinating with vendors. Azure Administrators use the Azure Portal and as they become more proficient they use PowerShell and the Command Line Interface. Overview After completing this course, students will be able to: Migrate and SAP HANA, S/4HANA, SAP NetWeaver to Azure Leverage Azure Portal, Cloud Shell, Azure PowerShell, CLI, and Resource Manager Use intersite connectivity features including VNet Peering and VNet-to-VNet connections Work with Azure Active Directory (AAD) and Azure AD Connect As an IT Professionals experienced in SAP solutions, you will discover how to leverage Azure resources that include deployment and configuration of virtual machines, virtual networks, storage accounts, and Azure AD that includes implementing and managing hybrid identities. You will also be introduced to concepts, scenarios, procedures, and hands-on labs that will empower you to best plan and implement migration and operation of an SAP solution on Azure. You will also receive guidance on subscriptions, create and scale virtual machines, implement storage solutions, configure virtual networking, back up and share data, connect Azure and on-premises sites, manage network traffic, implement Azure Active Directory, secure identities, and monitor your solution. Introduction Contains an overview of the SAP and Microsoft partnership. Foundations of SAP on Azure Contains brief lessons on Azure compute, Azure storage, Azure networking, SAP HANA for Azure (Large Instances), identity services, governance and manageability, backup and data protection services, and migration services. SAP Certified Offerings on Azure Contains lessons on general prerequisites (SAP support in public cloud environments), deployment options of Azure for SAP workloads, SAP product-specific support for Azure, operating System support of Azure for SAP workloads, storage support of Azure for SAP workloads, networking support for SAP, database support for SAP, high availability and disaster recovery support for SAP, and monitoring requirements for SAP. Lab : Online Lab: Implementing Linux clustering on Azure VMs Lab : Online Lab: Implementing Windows clustering on Azure VMs SAP on Azure Reference Architecture Contains lessons on SAP NetWeaver with AnyDB, SAP S4 HANA, and SAP HANA on Azure (Large Instances) on Azure VMs. Planning for Implementing SAP Solutions on Azure Contains lessons on Azure VM compute, network, and storage considerations. As well as Azure VM high availability and disaster recovery, Azure VM backup considerations, Azure VM monitoring considerations, Azure VM security considerations, and Azure VM authentication and access control considerations. Planning for Migrating SAP Workloads to Azure Contains lessons on strategies for migrating SAP systems to Microsoft Azure and SAP a workload planning and deployment checklist. Implementing Azure VM-based SAP Solutions Contains lessons on Azure VM deployment methodologies, single-instance implementations (2-tier or 3-tier), implementing HA SAP NetWeaver with AnyDB on Azure VMs, implementing HA SAP HANA on Azure VMs, configure the Azure Enhanced Monitoring Extension for SAP, and implementing AD and Azure AD-based authentication. Module 8-Deploying HANA Large Instances (HLI) Contains a lesson on implementing HANA Large Instances (HLI). Migrating SAP Workloads to Azure Contains lessons on migration options, DMO methodologies, cloud migration options, and Very Large Database Migration to Azure. Lab : Online Lab-Implement SAP architecture on Azure VMs running Linux Lab : Online Lab-Implement SAP architecture on Azure VMs running Windows Maintaining SAP on Azure Contains lessons on, remote management, performing backups and restores, OS and workload updates, vertical and horizontal scaling, and Disaster Recovery (DR). Monitoring and Troubleshooting SAP on Azure Contains lessons on monitoring Azure VMs, monitoring SAP HANA on Azure (Large Instances), and integrating SAP solutions with Microsoft cloud services.

Duration 3 Days 18 CPD hours This course is intended for This course is designed for Pardot Marketers and Salesforce Administrators responsible for designing, building, and implementing marketing workflows and reports in the Pardot Lightning App. It's also for Business Users looking to learn more about the Pardot Lightning App. This course is a great foundation builder for anyone looking to take the Salesforce Pardot Specialist Certification Exam. Overview When you complete this course, you will be able to: Enable the Pardot Lightning App. Explain the relationship created between Pardot and Salesforce once the Pardot Lightning App has been enabled. Generate leads with Pardot Lightning App's various lead generation tools including forms, landing pages, and custom redirects. Manage leads with Pardot Lightning App's lead management tools including page actions, automation rules, segmentation rules, dynamic lists, and completion actions. Engage leads with Pardot Lightning App's lead engagement tools including email, personalization, dynamic content, and Engagement Studio. Qualify leads with Pardot Lightning App's scoring and grading functionality. Interpret data generated via Pardot Lightning App's reporting capabilities. Design and execute successful end-to-end marketing workflows using the Pardot Lightning App. Discover how to drive more qualified leads, nurture prospects through the sales cycle, and sell more effectively using the Pardot Lightning App. In this 3-day class, Pardot experts will show you how to design and implement marketing workflows in the Pardot Lightning App to make data-driven decisions and drive your company?s business forward. Learn how to create and automate dynamic emails, generate and qualify leads, and leverage reports and data to boost sales. Course Introduction Review Course Objectives Set Learner Expectations Review Housekeeping Items Review Additional Course Resources Explore Course Case Study Salesforce Integration Understand the Pardot-Salesforce Relationship Sync Data Between Salesforce and Pardot Enable the Pardot Lightning App in Salesforce Create Custom Fields Review Salesforce Campaigns Administration Create a Pardot Dashboard Authenticate Your Email Sending Domains Sync with Third-Party Applications Using Connectors Restore Assets from the Recycle Bin Create and Assign Users and User Groups Understand Usage Governance Visitors and Prospects Understand Pardot Visitors Understand Pardot Prospects List Management Create List Types Organize Prospects Using Static Lists Test Emails Using Test Lists Personalization and Email Marketing Personalize Your Emails with HML and Advanced Dynamic Content Automate Email Marketing Create Email Templates See What Works Best for Your Audience Using AB Testing Track Email Performance Using Email Reports Forms and Landing Pages Capture Leads with Forms Convert Visitors to Leads Using Landing Pages Track Leads with Forms and Landing Page Reports Track Clicks Using Custom Redirects Create Custom Redirect Reports Lead Management Trigger Page Actions Automate Actions from a Marketing Element Using Completion Actions Create a List of Prospects and Apply a Segmentation Action Using Segmentation Rules Create Repeatable, Criteria-Based Automation Rules Create Dynamic Lists Choose an Automation Tool Lead Qualification Understand Prospect Scoring Grade Prospects Based on Profiles Lead Nurturing Build an Engagement Program Create Engagement Program Reports Additional course details: Nexus Humans Salesforce Drive Sales with the Pardot Lightning App (PDX101) 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 Salesforce Drive Sales with the Pardot Lightning App (PDX101) 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.

Workshop is designed to support participants in using the Six Stages Framework in Board development and Diversity, Equity and Inclusion

Duration 2 Days 12 CPD hours This course is intended for Data Protection OfficersData Protection ManagersAuditorsLegal Compliance OfficersSecurity ManagerInformation ManagersAnyone involved with data protection processes and programs Overview It will show the world that students know privacy laws and regulations and how to apply them, and that students know how to secure your place in the information economy. When students earn a CIPP credential, it means they?ve gained a foundational understanding of broad global concepts of privacy and data protection law and practice, including: jurisdictional laws, regulations and enforcement models; essential privacy concepts and principals; legal requirements for handling and transferring data and more. It will show the world that students know privacy laws and regulations and how to apply them, and that students know how to secure their place in the information economy. When students earn a CIPP credential, it means they've gained a foundational understanding of broad global concepts of privacy and data protection law and practice, including: jurisdictional laws, regulations and enforcement models; essential privacy concepts and principals; legal requirements for handling and transferring data and more. Common Principles and Approaches to Privacy This unit includes a brief discussion about the modern history of privacy, an introduction to types of information, an overview of information risk management and a summary of modern privacy principles. Jurisdiction and Industries This unit introduces the major privacy models employed around the globe and provides an overview of privacy and data protection regulation by jurisdictions and industry sectors. Information Security: Safeguarding Personal Information This unit presents introductions to information security, including definitions, elements, standards, and threats/ vulnerabilities, as well as introductions to information security management and governance, including frameworks, controls, cryptography and identity and access management (IAM). Online Privacy: Using Personal Information on Websites and with Other Internet-related Technologies This unit focuses on the web as a platform, as well as privacy considerations for sensitive online information, including policies and notices, access, security, authentication and data collection. Additional topics include children?s online privacy, email, searches, online marketing and advertising, social media, online assurance, cloud computing and mobile devices. Canadian Legal Framework This unit provides an introduction to the Canadian legal system. It includes enforcement agencies and their powers, privacy basics from a Canadian perspective and the underlying framework for Canadian privacy law and practice. Canadian Private-sector Privacy Laws This unit focuses on the Canadian legal system. It includes enforcement agencies and their powers, privacy basics from a Canadian perspective and the underlying framework for Canadian privacy law and practice. Canadian Public-sector Privacy Laws This unit highlights key concepts and practices related to the collection, retention, use, disclosure and disposal of personal information by federal, provincial and territorial governments. Health Information Privacy Laws This unit touches on the applicability and purpose of health information privacy laws. Private-sector Compliance Practices This unit delves into the components that make up compliance regulations, including Generally Accepted Privacy Principals and security breach notification, and also examines compliance track records and Federal Commissioner Findings. Public-sector Compliance Practices This unit presents the various methods that can be implemented for compliance in the public sector, such as privacy impact assessments and data sharing agreements. In addition, it discusses the challenges presented by digital information exchanges, as well as non-legislative considerations. Health-sector Compliance Practices This unit covers the issues presented with digital compliance in the health sector. Additional course details: Nexus Humans Certified Information Privacy Professional (CIPP/CAN) 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 Certified Information Privacy Professional (CIPP/CAN) 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 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 5 Days 30 CPD hours This course is intended for This course is suitable for anyone responsible for configuring, managing or supporting a Veeam Availability Suite v11 environment. This includes Senior Engineers and Architects responsible for creating architectures for Veeam environments. Overview After completing this course, attendees should be able to: Describe Veeam Availability Suite components usage scenarios and relevance to your environment. Effectively manage data availability in on-site, off-site, cloud and hybrid environments. Ensure both Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs) are met. Configure Veeam Availability Suite to ensure data is protected effectively. Adapt with an organization's evolving technical and business data protection needs. Ensure recovery is possible, effective, efficient, secure and compliant with business requirements. Provide visibility of the business data assets, reports and dashboards to monitor performance and risks. Design and architect a Veeam solution in a real-world environment Describe best practices, review an existing infrastructure and assess business/project requirements Identify relevant infrastructure metrics and perform component (storage, CPU, memory) quantity sizing Provide implementation and testing guidelines in line with designs Innovatively address design challenges and pain points, matching appropriate Veeam Backup & Replication features with requirements Veeam Certified Architect is the highest level of Veeam technical certifications. Engineers who complete both Veeam Availability Suite v11: Configuration and Management and Veeam Backup & Replication V11: Architecture and Design programs (courses + exams) will be granted with the 'Veeam Certified Architect' (VMCA) title by Veeam. Introduction Veeam Availability Suite v11: Configuration and Management Describe RTOs and RPOs, what they mean for your business, how to manage and monitor performance against them The 3-2-1 Rule and its importance in formulating a successful backup strategy Identify key Veeam Availability Suite components and describe their usage scenarios and deployment types Building backup capabilities Backup methods, the appropriate use cases and impact on underlying file systems Create, modify, optimize and delete backup jobs, including Agents and NAS Backup jobs. Explore different tools and methods to maximize environment performance Ensure efficiency by being able to select appropriate transport modes while being aware of the impact of various backup functions on the infrastructure Building replication capabilities Identify and describe the options available for replication and impacts of using them Create and modify replication jobs, outline considerations to ensure success Introduce the new Continuous Data Protection (CDP) policy Secondary backups Simple vs. advanced backup copy jobs, how to create and modify them using best practices to ensure efficient recovery Discuss using tapes for backups Advanced repository capabilities Ensure repository scalability using a capability such as SOBR on-premises and off-site including integration with cloud storage Ensure compatibility with existing deduplication appliances Introduce the new hardened repository Protecting data in the cloud Review how Veeam can protect the data of a cloud native application Review how Veeam Cloud Connect enables you to take advantage of cloud services built on Veeam Review how Veeam can be used to protect your Office 365 data Restoring from backup Ensure you have the confidence to use the correct restore tool at the right time for restoring VMs, bare metal and individual content such as files and folders Utilize Secure Restore to prevent the restoration of malware Describe how to use Staged Restore to comply with things like General Data Protection Regulation (GDPR) before releasing restores to production Identify, describe and utilize the different explores and instant recovery tools and features Recovery from replica Identify and describe in detail, failover features and the appropriate usage Develop, prepare and test failover plans to ensure recovery Disaster recovery from replica to meet a variety of real-world recovery needs Testing backup and replication Testing backups and replicas to ensure you can recover, what you need, when you need to Configure and setup virtual sandbox environments based on backup, replicas and storage snapshots Veeam Backup Enterprise Manager and Veeam ONE Introduce the concept of monitoring your virtual, physical and cloud environments with Veeam Backup Enterprise Manager and Veeam ONE? Configuration backup Locate, migrate or restore backup configuration Introduction Veeam Backup & Replication v11: Architecture and Design Review the architecture principles Explore what a successful architecture looks like Review Veeam?s architecture methodology Discovery Analyze the existing environment Uncover relevant infrastructure metrics Uncover assumptions and risks Identify complexity in the environment Conceptual design Review scenario and data from discovery phase Identify logical groups of objects that will share resources based on requirements Create a set of detailed tables of business and technical requirements, constraints, assumptions and risks Review infrastructure data with each product component in mind Create high level design and data flow Logical design Match critical components and features of VBR with requirements Create logical groupings Determine location of components and relationship to logical grouping Aggregate totals of component resources needed per logical grouping Calculate component (storage, CPU, memory) quantity sizing Physical/tangible design Convert the logical design into a physical design Physical hardware sizing Create a list of physical Veeam backup components Implementation and Governance Review physical design and implantation plan Review Veeam deployment hardening Describe the architect?s obligations to the implementation team Provide guidance on implementation specifics that relate to the design Validation and Iteration Provide framework for how to test the design Further develop the design according to a modification scenario

Duration 4 Days 24 CPD hours This course is intended for Students in this course are interested in implementing DevOps processes or in passing the Microsoft Azure DevOps Solutions certification exam. Overview After completing this course, students will be able to: Plan for the transformation with shared goals and timelines Select a project and identify project metrics and Key Performance Indicators (KPI's) Create a team and agile organizational structure Design a tool integration strategy Design a license management strategy (e.g., Azure DevOps and GitHub users) Design a strategy for end-to-end traceability from work items to working software Design an authentication and access strategy Design a strategy for integrating on-premises and cloud resources Describe the benefits of using Source Control Describe Azure Repos and GitHub Migrate from TFVC to Git Manage code quality including technical debt SonarCloud, and other tooling solutions Build organizational knowledge on code quality Explain how to structure Git repos Describe Git branching workflows Leverage pull requests for collaboration and code reviews Leverage Git hooks for automation Use Git to foster inner source across the organization Explain the role of Azure Pipelines and its components Configure Agents for use in Azure Pipelines Explain why continuous integration matters Implement continuous integration using Azure Pipelines Define Site Reliability Engineering Design processes to measure end-user satisfaction and analyze user feedback Design processes to automate application analytics Manage alerts and reduce meaningless and non-actionable alerts Carry out blameless retrospectives and create a just culture Define an infrastructure and configuration strategy and appropriate toolset for a release pipeline and application infrastructure Implement compliance and security in your application infrastructure Describe the potential challenges with integrating open-source software Inspect open-source software packages for security and license compliance Manage organizational security and compliance policies Integrate license and vulnerability scans into build and deployment pipelines Configure build pipelines to access package security and license ratings This course provides the knowledge and skills to design and implement DevOps processes and practices. Students will learn how to plan for DevOps, use source control, scale Git for an enterprise, consolidate artifacts, design a dependency management strategy, manage secrets, implement continuous integration, implement a container build strategy, design a release strategy, set up a release management workflow, implement a deployment pattern, and optimize feedback mechanisms. Module 1: Get started on a DevOps transformation journey Introduction to DevOps Choose the right project Describe team structures Choose the DevOps tools Plan Agile with GitHub Projects and Azure Boards Introduction to source control Describe types of source control systems Work with Azure Repos and GitHub Module 2: Development for enterprise DevOps Structure your Git Repo Manage Git branches and workflows Collaborate with pull requests in Azure Repos Explore Git hooks Plan foster inner source Manage Git repositories Identify technical debt Module 3: Implement CI with Azure Pipelines and GitHub Actions Explore Azure Pipelines Manage Azure Pipeline agents and pools Describe pipelines and concurrency Explore Continuous integration Implement a pipeline strategy Integrate with Azure Pipelines Introduction to GitHub Actions Learn continuous integration with GitHub Actions Design a container build strategy Module 4: Design and implement a release strategy Introduction to continuous delivery Explore release strategy recommendations Build a high-quality release pipeline Introduction to deployment patterns Implement blue-green deployment and feature toggles Implement canary releases and dark launching Implement A/B testing and progressive exposure deployment Module 5: Implement a secure continuous deployment using Azure Pipelines Create a release pipeline Provision and test environments Manage and modularize tasks and templates Automate inspection of health Manage application configuration data Integrate with identity management systems Implement application configuration Module 6: Manage infrastructure as code using Azure and DSC Explore infrastructure as code and configuration management Create Azure resources using Azure Resource Manager templates Create Azure resources by using Azure CLI Explore Azure Automation with DevOps Implement Desired State Configuration (DSC) Implement Bicep Module 7: Implement security and validate code bases for compliance Introduction to Secure DevOps Implement open-source software Software Composition Analysis Static analyzers OWASP and Dynamic Analyzers Security Monitoring and Governance Module 8: Design and implement a dependency management strategy Explore package dependencies Understand package management Migrate, consolidate, and secure artifacts Implement a versioning strategy Introduction to GitHub Packages Module 9: Implement continuous feedback Implement tools to track usage and flow Develop monitor and status dashboards Share knowledge within teams Design processes to automate application analytics Manage alerts, Blameless retrospectives and a just culture Additional course details: Nexus Humans AZ-400 Designing and Implementing Microsoft DevOps Solutions 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 AZ-400 Designing and Implementing Microsoft DevOps Solutions 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.