328 Computing & Software Development Tools courses in Staines-upon-Thames delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for System administrators and operators who are operating in the AWS Cloud Informational technology workers who want to increase the system operations knowledge. Overview Identify the AWS services that support the different phases of Operational Excellence, an AWS Well-Architected Framework pillar Manage access to AWS resources using AWS accounts and organizations and AWS Identity and Access Management (IAM) Maintain an inventory of in-use AWS resources by using AWS services, such as AWS Systems Manager, AWS CloudTrail, and AWS Config Develop a resource deployment strategy using metadata tags, Amazon Machine Images (AMIs), and AWS Control Tower to deploy and maintain an AWS cloud environment Automate resource deployment by using AWS services, such as AWS CloudFormation and AWS Service Catalog Use AWS services to manage AWS resources through CloudOps lifecycle processes, such as deployments and patches Configure a highly available cloud environment that uses AWS services, such as Amazon Route 53 and Elastic Load Balancing, to route traffic for optimal latency and performance Configure AWS Auto Scaling and Amazon EC2 Auto Scaling to scale out your cloud environment based on demand Use Amazon CloudWatch and associated features, such as alarms, dashboards, and widgets, to monitor your cloud environment Manage permissions and track activity in your cloud environment by using AWS services, such as AWS CloudTrail and AWS Config Deploy your resources to an Amazon Virtual Private Cloud (Amazon VPC), establish necessary connectivity to your Amazon VPC, and protect your resources from disruptions of service State the purpose, benefits, and appropriate use cases for mountable storage in your AWS Cloud environment Explain the operational characteristics of object storage in the AWS Cloud, including Amazon Simple Storage Service (Amazon S3) and Amazon S3 Glacier Build a comprehensive cost model to help gather, optimize, and predict your cloud costs by using services such as AWS Cost Explorer and the AWS Cost & Usage Report This course teaches systems operators and anyone performing cloud operations functions how to manage and operate automatable and repeatable deployments of networks and systems on AWS. You will learn about cloud operations functions, such as installing, configuring, automating, monitoring, securing, maintaining, and troubleshooting these services, networks, and systems. The course also covers specific AWS features, tools, and best practices related to these functions. Prerequisites Successfully completed the AWS Technical Essentials course Background in either software development or systems administration Proficiency in maintaining operating systems at the command line, such as shell scripting in Linux environments or cmd/PowerShell in Windows Basic knowledge of networking protocols (TCP/IP, HTTP) 1 - Introduction to Cloud Operations on AWS What is Cloud Operations AWS Well-Architected Framework AWS Well-Architected Tool 2 - Access Management AWS Identity and Access Management (IAM) Resources, accounts, and AWS Organizations 3 - System Discovery Methods to interact with AWS services Tools for automating resource discovery Inventory with AWS Systems Manager and AWS Config Hands-On Lab: Auditing AWS Resources with AWS Systems Manager and AWS Config 4 - Deploy and Update Resources Cloud Operations in deployments Tagging strategies Deployment using Amazon Machine Images (AMIs) Deployment using AWS Control Tower 5 - Automate Resource Deployment Deployment using AWS CloudFormation Deployment using AWS Service Catalog Hands-On Lab: Infrastructure as Code 6 - Manage Resources AWS Systems Manager Hands-On Lab: Operations as Code 7 - Configure Highly Available Systems Distributing traffic with Elastic Load Balancing Amazon Route 53 8 - Automate Scaling Scaling with AWS Auto Scaling Scaling with Spot Instances Managing licenses with AWS License Manager 9 - Monitor and Maintain System Health Monitoring and maintaining healthy workloads Monitoring AWS infrastructure Monitoring applications Hands-On Lab: Monitor Applications and Infrastructure 10 - Data Security and System Auditing Maintaining a strong identity and access foundation Implementing detection mechanisms Automating incident remediation 11 - Operate Secure and Resilient Networks Building a secure Amazon Virtual Private Cloud (Amazon VPC) Networking beyond the VPC 12 - Mountable Storage Configuring Amazon Elastic Block Store (Amazon EBS) Sizing Amazon EBS volumes for performance Using Amazon EBS snapshots Using Amazon Data Lifecycle Manager to manage your AWS resources Creating backup and data recovery plans Configuring shared file system storage Hands-On Lab: Automating with AWS Backup for Archiving and Recovery 13 - Object Storage Deploying Amazon Simple Storage Service (Amazon S3) Managing storage lifecycles on Amazon S3 14 - Cost Reporting, Alerts, and Optimization Gaining AWS cost awareness Using control mechanisms for cost management Optimizing your AWS spend and usage Hands-On Lab: Capstone lab for CloudOps Additional course details: Nexus Humans Cloud Operations on AWS 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 Cloud Operations on AWS 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 This course is intended for solutions architects, solution-design engineers, developers seeking an understanding of AWS architecting and individuals seeking the AWS Solutions Architect-Associate certification. Overview Identify AWS architecting basic practices. Explore using the AWS management tools: The AWS Console, Command Line Interface (CLI), and CloudFormation in a lab environment. Examine the enforcement of accounts security using policies. Identify the elements that build an elastic, secure, virtual network that includes private and public subnets. Practice building an AWS core networking infrastructure. Determine strategies for a layered security approach to Virtual Private Cloud (VPC) subnets. Identify strategies to select the appropriate compute resources based on business use-cases. Practice building a VPC and adding an Elastic Cloud Compute (EC2) instance in a lab environment. Practice installing an Amazon Relational Database Service (RDS) instance and an Application Load Balancer (ALB) in the VPC you created. Compare and contrast AWS storage products and services, based on business scenarios. Compare and contrast the different types of AWS database services based on business needs. Practice building a highly available, auto-scaling database layer in a lab. Explore the business value of AWS monitoring solutions. Identify the role of monitoring, event driven load balancing, and auto scaling responses, based on usage and needs. Identify and discuss AWS automation tools that will help you build, maintain and evolve your infrastructure. Discuss network peering, VPC endpoints, gateway and routing solutions based on use-cases. Discuss hybrid networking configurations to extend and secure your infrastructure. Discuss the benefits of microservices as an effective decoupling strategy to power highly available applications at scale. Explore AWS container services for the rapid implementation of an infrastructure-agnostic, portable application environment. Identify the business and security benefits of AWS serverless services based on business examples. Practice building a serverless infrastructure in a lab environment. Discuss the ways in which AWS edge services address latency and security. Practice building a CloudFront deployment with an S3 backend in a lab environment. Explore AWS backup, recovery solutions, and best practices to ensure resiliency and business continuity. Build a highly available and secure cloud architecture based on a business problem, in a project-based facilitator-guided lab. Architecting on AWS is for solutions architects, solution-design engineers, and developers seeking an understanding of AWS architecting. In this course, you will learn to identify services and features to build resilient, secure and highly available IT solutions on the AWS Cloud. Architectural solutions differ depending on industry, types of applications, and business size. AWS Authorized Instructors emphasize best practices using the AWS Well-Architected Framework, and guide you through the process of designing optimal IT solutions, based on real-life scenarios. The modules focus on account security, networking, compute, storage, databases, monitoring, automation, containers, serverless architecture, edge services, and backup and recovery. At the end of the course, you will practice building a solution and apply what you have learned with confidence. Prerequisites AWS Cloud Practitioner Essentials classroom or digital training, or Working knowledge of distributed systems Familiarity with general networking concepts Familiarity with IP addressing Working knowledge of multi-tier architectures Familiarity with cloud computing concepts 0 - Introductions & Course Map review Welcome and course outcomes 1 - Architecting Fundamentals Review AWS Services and Infrastructure Infrastructure Models AWS API Tools Securing your infrastructure The Well-Architected Framework Hands-on lab: Explore Using the AWS API Tools to Deploy an EC2 Instance 2 - Account Security Security Principals Identity and Resource-Based Policies Account Federation Introduction to Managing Multiple Accounts 3 - Networking, Part 1 IP Addressing Amazon Virtual Private Cloud (VPC), Patterns and Quotas Routing Internet Access Network Access Control Lists (NACLs) Security Groups 4 - Compute Amazon Elastic Cloud Compute (EC2) EC2 Instances and Instance Selection High Performance Computing on AWS Lambda and EC2, When to Use Which Hands-On Lab: Build Your Amazon VPC Infrastructure 5 - Storage Amazon S3, Security, Versioning and Storage Classes Shared File Systems Data Migration Tools 6 - Database Services AWS Database Solutions Amazon Relational Database Services (RDS) DynamoDB, Features and Use Cases Redshift, Features, Use Cases and Comparison with RDS Caching and Migrating Data Hands-on Lab: Create a Database Layer in Your Amazon VPC Infrastructure 7 - Monitoring and Scaling Monitoring: CloudWatch, CloudTrail, and VPC Flow Logs Invoking Events 8 - Automation CloudFormation AWS Systems Manager 9 - Containers Microservices Monitoring Microservices with X-Ray Containers 10 - Networking Part 2 VPC Peering & Endpoints Transit Gateway Hybrid Networking Route 53 11 - Serverless Architecture Amazon API Gateway Amazon SQS, Amazon SNS Amazon Kinesis Data Streams & Kinesis Firehose Step Functions Hands-on Lab: Build a Serverless Architecture 12 - Edge Services Edge Fundamentals Amazon CloudFront AWS Global Accelerator AWS Web Application Firewall (WAF), DDoS and Firewall Manager AWS Outposts Hands-On Lab: Configure an Amazon CloudFront Distribution with an Amazon S3 Origin 13 - Backup and Recovery Planning for Disaster Recovery AWS Backup Recovery Strategie Additional course details: Nexus Humans Architecting on AWS 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 on AWS 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.

DASA DevOps Fundamentals: In-House Training The DASA DevOps Fundamentals™ certification provides the core education necessary to build your DevOps vocabulary and understand its principles and practices. It's the ideal starting point for DevOps journeys, whether you're already familiar with working with Agile and/or DevOps teams or not. Faster software deployment, increased deployment frequency, and higher change success rate are only some of the visible outcomes of practicing DevOps. Organizations such as Netflix, Spotify, and Facebook are transforming IT by successfully implementing DevOps principles. But you don't have to be big to be a DevOps leader. Companies large and small, young and old, have smoothly made the transition and have the proof of success in their pockets. This course will inspire you to serve as a change champion by sharing and using what you learned, and continue to learn, about DevOps to lead and mentor others. A solid understanding of DevOps Fundamentals has helped numerous professionals and organizations how to approach a DevOps journey, not only from a tool and automation perspective but also looking in-depth at the softer side of things. This course provides learners with an extensive introduction to the core Agile DevOps principles. It covers all 12 key knowledge and skill competencies defined by DASA to ensure you acquire a solid knowledge of DevOps concepts and terminology. Multiple cases or scenarios, group discussions, and examples are included in the course to enhance your learning experience. What you will Learn DASA DevOps Fundamentals-certified professionals are able to: Explain the drivers responsible for the emergence of DevOps. Define and discuss the key concepts and principles of DevOps. List and explain the business benefits of DevOps and continuous delivery. Know how teams can translate DevOps principles into tangible practices. Learn about modern operations in a DevOps context. Explain the concepts of test automation, infrastructure automation, and build and deployment automation. Describe how DevOps relates to Lean and Agile methodologies. Get insight into the various organizational DevOps models and architectures. Identify how Cloud and Delivery pipeline automation optimizes and accelerates the ways of working. Discuss the critical success factors for DevOps transformation. Introducing DASA DevOps Fundamentals DASA DevOps Fundamentals An Introduction Case Study - Easy Journey Airways Building the DevOps Context DevOps Evolution Business Benefits of DevOps DASA DevOps Principles Goals and Measurements Knowing DevOps for Individuals T-Shape Profiles DevOps Capabilities by DASA DASA DevOps Certifications Getting Acquainted with DevOps Culture and Behavior Embracing a DevOps Culture Core Elements of a DevOps Culture Implementation of a DevOps Culture Understanding the Value of DevOps for Teams and Organizations Organizational Models Team Autonomy DevOps at Scale Getting Familiar with DevOps Management Practices ITSM Lean Agile Getting Familiar with DevOps Technical Practices Architecture Modern Infrastructure and Cloud Operations Enabling DevOps Team Performance Through Continuous Delivery and Automation Software Delivery Automation Concepts Continuous Delivery Core Concepts Continuous Delivery Automation Concepts Continuous Delivery Automation Focus Topics Measuring the Performance - The Next Steps Analyze the Current Situation Improve Incrementally

Lean Six Sigma Yellow Belt Certification Program: In-House Training This course is designed to instill an in-depth understanding of Lean Six Sigma and a clear sense of what is required to define high-impact improvement projects, establish Lean Six Sigma measurements, and complete Lean Six Sigma projects using the systematic and proven Define, Measure, Analyze, Improve, and Control (DMAIC) methodology. This course is designed to instill an in-depth understanding of Lean Six Sigma and a clear sense of what is required to define high-impact improvement projects, establish Lean Six Sigma measurements, and complete Lean Six Sigma projects using the systematic and proven Define, Measure, Analyze, Improve, and Control (DMAIC) methodology. Participants will learn basic tools and techniques of Lean Six Sigma and those who pass a thirty-question exam (70% or above) will become a Certified Lean Six Sigma Yellow Belt. This course is delivered through four 3-hour online sessions. What you Will Learn You'll learn how to: Establish the structure that supports and sustains Lean Six Sigma Quality Identify and calculate key Lean Six Sigma Measurements (Sigma, DPMO, and Yield) Select successful, high-impact projects that match strategic objectives Document, measure, and improve key processes using the DMAIC (Define, Measure, Analyze, Improve, and Control) Methodology Utilize data-based thinking to make key business decisions Introduction to the Fundamentals and Vision of Lean Six Sigma Lean Six Sigma's focus on the customer, on quality, and on results The costs of poor quality Critical factors to consider when deploying Lean Six Sigma Lean Six Sigma as a process improvement methodology Lean Six Sigma metrics Why do it - ROI and payback for Lean Six Sigma Business Process Management Critical Lean Six Sigma roles and responsibilities Main aspects of managing the organizational change Project selection Metrics of Lean Six Sigma and the DMAIC Model How to strategically align business metrics and projects within an organization How to identify and measure quality characteristics which are critical to customers What does the customer (internal or external) really want from our products and services? Establishing appropriate teams and setting those teams up to be successful What defines a good measurement system? How are we doing (learning the secret to measuring the right things, right)? How to improve output measures by understanding and measuring the process Where are there defects (how to properly select and scope high-impact projects)? Where is the process broken (the Lean Six Sigma version of root cause analysis)? How to determine the process efficiency, or value add, of a process The appropriate use of quality tools Understanding the concept of variation and how to reduce knee-jerk reactions How to achieve breakthrough results for any key measure How can we ensure the identified improvements will be sustainable (the basics of process control)?

DASA DevOps Fundamentals: Virtual In-House Training The DASA DevOps Fundamentals™ certification provides the core education necessary to build your DevOps vocabulary and understand its principles and practices. It's the ideal starting point for DevOps journeys, whether you're already familiar with working with Agile and/or DevOps teams or not. Faster software deployment, increased deployment frequency, and higher change success rate are only some of the visible outcomes of practicing DevOps. Organizations such as Netflix, Spotify, and Facebook are transforming IT by successfully implementing DevOps principles. But you don't have to be big to be a DevOps leader. Companies large and small, young and old, have smoothly made the transition and have the proof of success in their pockets. This course will inspire you to serve as a change champion by sharing and using what you learned, and continue to learn, about DevOps to lead and mentor others. A solid understanding of DevOps Fundamentals has helped numerous professionals and organizations how to approach a DevOps journey, not only from a tool and automation perspective but also looking in-depth at the softer side of things. This course provides learners with an extensive introduction to the core Agile DevOps principles. It covers all 12 key knowledge and skill competencies defined by DASA to ensure you acquire a solid knowledge of DevOps concepts and terminology. Multiple cases or scenarios, group discussions, and examples are included in the course to enhance your learning experience. What you will Learn DASA DevOps Fundamentals-certified professionals are able to: Explain the drivers responsible for the emergence of DevOps. Define and discuss the key concepts and principles of DevOps. List and explain the business benefits of DevOps and continuous delivery. Know how teams can translate DevOps principles into tangible practices. Learn about modern operations in a DevOps context. Explain the concepts of test automation, infrastructure automation, and build and deployment automation. Describe how DevOps relates to Lean and Agile methodologies. Get insight into the various organizational DevOps models and architectures. Identify how Cloud and Delivery pipeline automation optimizes and accelerates the ways of working. Discuss the critical success factors for DevOps transformation. Introducing DASA DevOps Fundamentals DASA DevOps Fundamentals An Introduction Case Study - Easy Journey Airways Building the DevOps Context DevOps Evolution Business Benefits of DevOps DASA DevOps Principles Goals and Measurements Knowing DevOps for Individuals T-Shape Profiles DevOps Capabilities by DASA DASA DevOps Certifications Getting Acquainted with DevOps Culture and Behavior Embracing a DevOps Culture Core Elements of a DevOps Culture Implementation of a DevOps Culture Understanding the Value of DevOps for Teams and Organizations Organizational Models Team Autonomy DevOps at Scale Getting Familiar with DevOps Management Practices ITSM Lean Agile Getting Familiar with DevOps Technical Practices Architecture Modern Infrastructure and Cloud Operations Enabling DevOps Team Performance Through Continuous Delivery and Automation Software Delivery Automation Concepts Continuous Delivery Core Concepts Continuous Delivery Automation Concepts Continuous Delivery Automation Focus Topics Measuring the Performance - The Next Steps Analyze the Current Situation Improve Incrementally

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Duration 3 Days 18 CPD hours This course is intended for This course is intended for Solution Architects Overview At the end of this course, you will be able to: Apply the AWS Well-Architected Framework Manage multiple AWS accounts for your organization Connect an on-premises datacenter to AWS cloud Move large data from an on-premises datacenter to AWS Design large datastores for AWS cloud Understand different architectural designs for scalability Protect your infrastructure from DDoS attack Secure your data on AWS with encryption Enhance the performance of your solutions Select the most appropriate AWS deployment mechanism Building on concepts introduced in Architecting on AWS, Advanced Architecting on AWS is intended for individuals who are experienced with designing scalable and elastic applications on the AWS platform. Building on concepts introduced in Architecting on AWS, this course covers how to build complex solutions which incorporate data services, governance, and security on AWS. This course introduces specialized AWS services, including AWS Direct Connect and AWS Storage Gateway to support Hybrid architecture. It also covers designing best practices for building scalable, elastic, secure, and highly available applications on AWS. Module 1: AWS Account Management Multiple accounts Multi-account patterns License management Manage security and costs with multiple accounts AWS Organizations AWS Directory Service Hands-on lab: Multi-VPC connectivity using a VPN Module 2: Advanced Network Architectures Improve VPC network connections Enhance performance for HPC workloads VPN connections over AWS AWS Direct Connect AWS Transit Gateway Amazon Route 53 Exercise: Design a hybrid architecture Module 3: Deployment Management on AWS Application lifecycle management Application deployment using containers AWS Elastic Beanstalk AWS OpsWorks AWS CloudFormation Module 4: Data Optimize Amazon S3 storage Amazon ElastiCache AWS Snowball AWS Storage Gateway AWS DataSync Backup and archival considerations Database migration Designing for big data with Amazon DynamoDB Hands-on lab: Build a failover solution with Amazon Route 53 and Amazon RDS Module 5: Designing for large scale applications AWS Auto Scaling Migrating over-provisioned resources Blue-green deployments on AWS Hands-on lab: Blue-green deployment with AWS Module 6: Building resilient architectures DDoS attack overview AWS Shield AWS WAF Amazon GuardDuty High availability using Microsoft SQL Server and Microsoft SharePoint on AWS High availability using MongoDB on Amazon EC2 AWS Global Accelerator Hands-on lab: CloudFront content delivery and automating AWS WAF rules Module 7: Encryption and data security Encryption primer DIY key management in AWS AWS Marketplace for encryption products AWS Key Management Service (AWS KMS) Cloud Hardware Security Module (HSM) Comparison of key management options Hands-on lab: AWS KMS with envelope encryption

Duration 2 Days 12 CPD hours This course is intended for The primary audience for this course is any IT, facilities or data centre professional who works in and around the data centre and who has the responsibility to achieve and improve the availability and manageability of the data centre. Overview After completion of the course the participant will be able to:? Choose an optimum site for mission-critical data centre based on current and future needs? Describe all components that are important for high availability in a data centre and how to effectively setup the data centre? Name and apply the various industry standards? Describe the various technologies for UPS, fire suppression, cooling, monitoring systems, cabling standards, etc, and to select and apply them effectively to cost-efficiently enhance the high-availability of the data centre.? Review the electrical distribution system to avoid costly downtime? Enhance cooling capabilities and efficiency in the data centre by using existing and new techniques and technologies for the increased cooling requirements of the future? Design a highly reliable and scalable network architecture and learn how to ensure installers apply proper testing techniques? Create effective maintenance contracts with equipment suppliers ensuring the best return on investment? Setup effective data centre monitoring ensuring the right people get the right message? Ensure proper security measures, both procedural and technical, are established to safeguard your company's valuable information in the data centre The course will address how to setup and improve key aspects such as power, cooling, security, cabling, safety, etc., to ensure a high available data centre. It will also address key operations and maintenance aspects. The Data Centre, it?s Importance and Causes for DowntimeData Centre Standards and Best PracticesData Centre Location, Building and Construction Selecting appropriate sites and buildings and how to avoid pitfalls Various components of an effective data centre and supporting facilities setup Raised Floor/Suspended Ceiling Uniform, concentrated and rolling load definitions Applicable standards Raised Floor guidelines Signal Reference Grid, grounding of racks Disability act and regulations Suspended ceiling usage and requirements Light Standards Light fixture types and placement Emergency lighting, Emergency Power Supply (EPS) Power Infrastructure Power infrastructure layout from generation to rack level ATS and STS systems Redundancy levels and techniques Three-phase and single-phase usage Power distribution options within the computer room Power cabling versus bus bar trunking Bonding versus grounding Common Mode Noise and isolation transformers Distribution boards, form factors and IP-protection grades Power quality guidelines Real power versus apparent power How to size and calculate load in the data centre Generators Static and dynamic UPS systems, selection criteria, how they operate and energy efficiency option Battery types, correct selection and testing Thermo-graphics Electro Magnetic Fields Electrical fields and magnetic fields definitions and units of measurements Sources of EMF Effects of EMF on human health and equipment (H)EMP Standards EMF shielding solutions Equipment Racks Rack standards, properties and selection criteria Security considerations Power rail/strip options Cooling Infrastructure Temperature and humidity recommendations Cooling measurement units and conversion rates Sensible and latent heat definitions Differences between comfort and precision cooling Overview of different air conditioner technologies Raised floor versus non-raised floor cooling Placement of air conditioner units and limitations to be observed Supplemental cooling options Cold aisle/hot aisle containment Water Supply Importance of water supply and application areas Backup water supply techniques Designing a Scalable Network Infrastructure The importance of a Structured Cabling System Planning considerations Copper and Fiber cable technology and standards ANSI/TIA-942 Cabling hierarchy and recommendations Testing and verification SAN storage cabling Network redundancy Building-to-building connectivity Network monitoring system requirements Fire Suppression Standards for fire suppression Detection systems Various total flooding fire suppression techniques and systems, their benefits and disadvantages Handheld extinguishers Signage and safety Regulatory requirements and best practices Data Centre Monitoring Data centre monitoring requirements EMS versus BMS Water leak detection systems Notification options and considerations Operational Security and Safety Practices Data centre security layers Physical, infrastructure and organisational security Safety measures and essential signage Labelling Choosing a labelling scheme Recommended labelling practices Network labelling Documentation How to setup proper documentation Document management policies and procedures Cleaning Cleaning practices for the data centre MTBF/MTTR Standards and definitions Calculation models The ?real? value Maintenance Contracts/SLA/OLAEXAM: Certified Data Centre Professional Additional course details: Nexus Humans Certified Data Centre Professional (CDCP) 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 Data Centre Professional (CDCP) 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 1 Days 6 CPD hours This course is intended for The primary audience for this course is data professionals who are familiar with data modeling, extraction, and analytics. It is designed for professionals who are interested in gaining knowledge about Lakehouse architecture, the Microsoft Fabric platform, and how to enable end-to-end analytics using these technologies. Job role: Data Analyst, Data Engineer, Data Scientist Overview Describe end-to-end analytics in Microsoft Fabric Describe core features and capabilities of lakehouses in Microsoft Fabric Create a lakehouse Ingest data into files and tables in a lakehouse Query lakehouse tables with SQL Configure Spark in a Microsoft Fabric workspace Identify suitable scenarios for Spark notebooks and Spark jobs Use Spark dataframes to analyze and transform data Use Spark SQL to query data in tables and views Visualize data in a Spark notebook Understand Delta Lake and delta tables in Microsoft Fabric Create and manage delta tables using Spark Use Spark to query and transform data in delta tables Use delta tables with Spark structured streaming Describe Dataflow (Gen2) capabilities in Microsoft Fabric Create Dataflow (Gen2) solutions to ingest and transform data Include a Dataflow (Gen2) in a pipeline This course is designed to build your foundational skills in data engineering on Microsoft Fabric, focusing on the Lakehouse concept. This course will explore the powerful capabilities of Apache Spark for distributed data processing and the essential techniques for efficient data management, versioning, and reliability by working with Delta Lake tables. This course will also explore data ingestion and orchestration using Dataflows Gen2 and Data Factory pipelines. This course includes a combination of lectures and hands-on exercises that will prepare you to work with lakehouses in Microsoft Fabric. Introduction to end-to-end analytics using Microsoft Fabric Explore end-to-end analytics with Microsoft Fabric Data teams and Microsoft Fabric Enable and use Microsoft Fabric Knowledge Check Get started with lakehouses in Microsoft Fabric Explore the Microsoft Fabric Lakehouse Work with Microsoft Fabric Lakehouses Exercise - Create and ingest data with a Microsoft Fabric Lakehouse Use Apache Spark in Microsoft Fabric Prepare to use Apache Spark Run Spark code Work with data in a Spark dataframe Work with data using Spark SQL Visualize data in a Spark notebook Exercise - Analyze data with Apache Spark Work with Delta Lake Tables in Microsoft Fabric Understand Delta Lake Create delta tables Work with delta tables in Spark Use delta tables with streaming data Exercise - Use delta tables in Apache Spark Ingest Data with DataFlows Gen2 in Microsoft Fabric Understand Dataflows (Gen2) in Microsoft Fabric Explore Dataflows (Gen2) in Microsoft Fabric Integrate Dataflows (Gen2) and Pipelines in Microsoft Fabric Exercise - Create and use a Dataflow (Gen2) in Microsoft Fabric

Duration 1 Days 6 CPD hours This course is intended for The audience for this course is individuals who want to learn the fundamentals of database concepts in a cloud environment, get basic skilling in cloud data services, and build their foundational knowledge of cloud data services within Microsoft Azure. Overview Describe core data concepts Identify considerations for relational data on Azure Describe considerations for working with non-relational data on Azure Describe an analytics workload on Azure In this course, students will gain foundational knowledge of core data concepts and related Microsoft Azure data services. Students will learn about core data concepts such as relational, non-relational, big data, and analytics, and build their foundational knowledge of cloud data services within Microsoft Azure. Students will explore fundamental relational data concepts and relational database services in Azure. They will explore Azure storage for non-relational data and the fundamentals of Azure Cosmos DB. Students will learn about large-scale data warehousing, real-time analytics, and data visualization. 1 - Explore core data concepts Identify data formats Explore file storage Explore databases Explore transactional data processing Explore analytical data processing 2 - Explore data roles and services Explore job roles in the world of data Identify data services 3 - Explore fundamental relational data concepts Understand relational data Understand normalization Explore SQL Describe database objects 4 - Explore relational database services in Azure Describe Azure SQL services and capabilities Describe Azure services for open-source databases 5 - Explore Azure Storage for non-relational data Explore Azure blob storage Explore Azure DataLake Storage Gen2 Explore Azure Files Explore Azure Tables 6 - Explore fundamentals of Azure Cosmos DB Describe Azure Cosmos DB Identify Azure Cosmos DB APIs 7 - Explore fundamentals of large-scale data warehousing Describe data warehousing architecture Explore data ingestion pipelines Explore analytical data stores 8 - Explore fundamentals of real-time analytics Understand batch and stream processing Explore common elements of stream processing architecture Explore Azure Stream Analytics Explore Apache Spark on Microsoft Azure 9 - Explore fundamentals of data visualization Describe Power BI tools and workflow Describe core concepts of data modeling Describe considerations for data visualization