679 PIP courses in Bradford delivered Online

Duration 5 Days 30 CPD hours This course is intended for This class is designed for experienced BizTalk Server Developers who have at least one year of hands-on experience developing BizTalk Server applications. Overview In this 5-day course, you will learn how to apply best practices and design patterns to build smarter BizTalk Server applications. Furthermore, this course provides extensive coverage of BizTalk Server's extensibility, including such topics as: custom functoids, custom pipeline components, and invoking external .NET methods. This course is designed specifically for experienced BizTalk Server developers and focuses on best practices & pattern-based design while pulling back the curtain on some of BizTalk Server's eccentricities. Review of BizTalk Server Fundamentals The BizTalk Server Architecture Inner Workings of the Messaging Engine Messaging Engine Deep Dive Two-way Messaging Without Orchestrations Designing and Testing Schemas Schema Design Enabling Unit Testing for BizTalk Projects Data Translation and Transformation Custom Data Transformation Creating Custom Pipeline Components Working with Message Interchanges Debatching Message Interchanges Advanced Concepts of WCF Adapters Connecting to External Systems Using WCF LOB Adapters in BizTalk Server Publishing and Consuming WCF and RESTful Services Overview of Service Integration Using WCF Implementing WCF Services Preprocessing Messages with IIS Modules Consuming Services Advanced Orchestration Communication Patterns Orchestration Engine Deep Dive Splitting and Aggregating Messages using Orchestrations Orchestration Communication Bridging the Synchronous/Asynchronous Gap Across Multiple Channels Correlating Messages in Orchestration Instances Building Convoy Orchestrations Handling Orchestration Faults and Exceptions Exception Handling in Orchestrations Implementing Transactions and Compensation Creating Transactional Processes Designing Custom Tracking Models for BizTalk Applications Introduction to Business Activity Monitoring Enabling Business Activity Monitoring Extending BAM Beyond BizTalk Building Declarative Logic Using the Business Rules Engine Concepts of Declarative Logic Fundamentals of BizTalk BRE Integrating Policies with BizTalk Advanced Concepts of the Business Rules Engine Advanced Business Rule Concepts Working with Advanced Facts Integrating Across Business Boundaries Using Parties, Roles, and EDI Port Binding Option Review Role-Based Integration What is EDI? Enabling EDI-Based Messaging

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course is individuals who administer and maintain SQL Server databases. These individuals perform database administration and maintenance as their primary area of responsibility, or work in environments where databases play a key role in their primary job. The secondary audiences for this course are individuals who develop applications that deliver content from SQL Server databases. Overview After completing this course, you will be able to: Authenticate and authorize users Assign server and database roles Authorize users to access resources Use encryption and auditing features to protect data Describe recovery models and backup strategies Backup and Restore SQL Server databases Automate database management Configure security for the SQL Server agent Manage alerts and notifications Managing SQL Server using PowerShell Trace access to SQL Server Monitor a SQL Server infrastructure Troubleshoot a SQL Server infrastructure Import and export data This course will provide training in how to administer and maintain SQL Server, Azure SQL Database and other cloud-based SQL Server databases. IT professionals who work in environments where databases play a key role in their job will find this material useful. By using demonstrations and hands-on lab exercises, students will learn to carry out these important tasks. This course covers content that was in retired Microsoft Course 20764: Administering a SQL Database Infrastructure. 1 - Creating advanced functions Lesson 1: Converting a command into an advanced function Lesson 2: Creating a script module Lesson 3: Defining parameter attributes and input validation Lesson 4: Writing functions that accept pipeline input Lesson 5: Producing complex pipeline output Lesson 6: Using comment-based Help Lesson 7: Using Whatif and Confirm parameters 2 - Using Microsoft .NET Framework and REST API in Windows PowerShell Lesson 1: Using .NET Framework in PowerShell Lesson 2: Using REST API in PowerShell 3 - Writing controller scripts Lesson 1: Understanding controller scripts Lesson 2: Writing controller scripts with a user interface Lesson 3: Writing controller scripts that create reports 4 - Handling script errors Lesson 1: Understanding error handling Lesson 2: Handling errors in a script 5 - Using XML, JSON, and custom-formatted data Lesson 1: Working with XML formatted data Lesson 2: Working with JSON formatted data Lesson 3: Working with custom-formatted data 6 - Enhancing server management with Desired State Configuration and Just Enough Administration Lesson 1: Implementing Desired State Configuration Lesson 2: Implementing Just Enough Administration 7 - Analyzing and debugging scripts Lesson 1: Debugging in Windows PowerShell Lesson 2: Analyzing and debugging an existing script 8 - Understanding Windows PowerShell Workflow Lesson 1: Understanding Windows PowerShell Workflows Lesson 2: Running Windows PowerShell Workflows

Duration 5 Days 30 CPD hours This course is intended for The Messaging Administrator deploys, configures, manages, and troubleshoots recipients, permissions, mail protection, mail flow, and public folders in both on-premises and cloud enterprise environments. Responsibilities include managing message hygiene, messaging infrastructure, and hybrid configuration and migration. To implement a secure hybrid topology that meets the business needs of a modern organization, the Messaging Administrator must collaborate with the Security Administrator and Microsoft 365 Enterprise Administrator. The Messaging Administrator should have a working knowledge of authentication types, licensing, and integration with Microsoft 365 applications. Overview After completing this course, students will be able to: Configure and manage the transport pipeline Manage and troubleshoot mail flow and transport issues Manage message hygiene and compliance Manage authentication for messaging Configure organizational settings and sharing Manage mobile devices Manage role-based permissions Create and manage recipient objects and resources Plan, implement, and troubleshoot public folders Plan a hybrid environment Perform mailbox migrations Deploy and troubleshoot a hybrid environment This course examines the key elements of Microsoft 365 messaging administration, including message transport and mail flow, messaging security, hygiene, and compliance, messaging infrastructure, and hybrid messaging. This course is designed for IT Professionals who deploy and manage the messaging infrastructure for Microsoft 365 in their organization. Managing the Transport Pipeline Overview of Transport Services Configuring Message Transport Managing Transport Rules Managing and Troubleshooting Mail Flow Managing Mail Flow Troubleshooting Mail Flow Troubleshooting Transport Issues Troubleshooting with Logs Managing Message Hygiene Planning for Message Hygiene Managing Anti-Malware and Anti-Spam Policies Managing Advanced Threat Protection Managing Compliance Messaging Compliance in the SCC Messaging Compliance in Exchange Managing Exchange Online Archiving and Auditing Managing Content Search Managing Organizational Settings Managing Authentication for Messaging Configuring Organizational Settings Configuring Organizational Sharing Managing Mobile Devices Mobile Device Mailbox Policies Managing Mobile Device Access Managing Role-Based Permissions Managing Admin Roles Managing User Roles Exchange Setup - RBAC and AD Split Permission Managing Recipient Objects and Resources Exchange Recipients Creating and Managing Exchange Recipients Managing Email Addresses, Lists, and Resources Managing Public Folders Planning the Public Folder Hierarchy Implementing and Managing Public Folders Troubleshooting Public Folders Planning a Hybrid Environment Exchange Hybrid Deployment Requirements Planning to Run the Hybrid Configuration Wizard Performing Mailbox Migrations Planning Mailbox Migrations Performing IMAP Migrations Performing Cutover and Staged Migrations Performing Advanced Migrations Deploying and Troubleshooting a Hybrid Environment Deploying and Managing an Edge Transport Server Configuring a Hybrid Deployment using the HCW Implementing Advanced Hybrid Functionality Troubleshooting Hybrid Deployments

Duration 4 Days 24 CPD hours This course is intended for This course is best suited to developers, engineers, and architects who want to use use Hadoop and related tools to solve real-world problems. Overview Skills learned in this course include:Creating a data set with Kite SDKDeveloping custom Flume components for data ingestionManaging a multi-stage workflow with OozieAnalyzing data with CrunchWriting user-defined functions for Hive and ImpalaWriting user-defined functions for Hive and ImpalaIndexing data with Cloudera Search Cloudera University?s four-day course for designing and building Big Data applications prepares you to analyze and solve real-world problems using Apache Hadoop and associated tools in the enterprise data hub (EDH). IntroductionApplication Architecture Scenario Explanation Understanding the Development Environment Identifying and Collecting Input Data Selecting Tools for Data Processing and Analysis Presenting Results to the Use Defining & Using Datasets Metadata Management What is Apache Avro? Avro Schemas Avro Schema Evolution Selecting a File Format Performance Considerations Using the Kite SDK Data Module What is the Kite SDK? Fundamental Data Module Concepts Creating New Data Sets Using the Kite SDK Loading, Accessing, and Deleting a Data Set Importing Relational Data with Apache Sqoop What is Apache Sqoop? Basic Imports Limiting Results Improving Sqoop?s Performance Sqoop 2 Capturing Data with Apache Flume What is Apache Flume? Basic Flume Architecture Flume Sources Flume Sinks Flume Configuration Logging Application Events to Hadoop Developing Custom Flume Components Flume Data Flow and Common Extension Points Custom Flume Sources Developing a Flume Pollable Source Developing a Flume Event-Driven Source Custom Flume Interceptors Developing a Header-Modifying Flume Interceptor Developing a Filtering Flume Interceptor Writing Avro Objects with a Custom Flume Interceptor Managing Workflows with Apache Oozie The Need for Workflow Management What is Apache Oozie? Defining an Oozie Workflow Validation, Packaging, and Deployment Running and Tracking Workflows Using the CLI Hue UI for Oozie Processing Data Pipelines with Apache Crunch What is Apache Crunch? Understanding the Crunch Pipeline Comparing Crunch to Java MapReduce Working with Crunch Projects Reading and Writing Data in Crunch Data Collection API Functions Utility Classes in the Crunch API Working with Tables in Apache Hive What is Apache Hive? Accessing Hive Basic Query Syntax Creating and Populating Hive Tables How Hive Reads Data Using the RegexSerDe in Hive Developing User-Defined Functions What are User-Defined Functions? Implementing a User-Defined Function Deploying Custom Libraries in Hive Registering a User-Defined Function in Hive Executing Interactive Queries with Impala What is Impala? Comparing Hive to Impala Running Queries in Impala Support for User-Defined Functions Data and Metadata Management Understanding Cloudera Search What is Cloudera Search? Search Architecture Supported Document Formats Indexing Data with Cloudera Search Collection and Schema Management Morphlines Indexing Data in Batch Mode Indexing Data in Near Real Time Presenting Results to Users Solr Query Syntax Building a Search UI with Hue Accessing Impala through JDBC Powering a Custom Web Application with Impala and Search

Duration 3 Days 18 CPD hours This course is intended for Senior Executives CIOs and CTOs Business Intelligence Executives Marketing Executives Data & Business Analytics Specialists Innovation Specialists & Entrepreneurs Academics, and other people interested in Big Data Overview More specifically, BDAW addresses advanced big data architecture topics, including, data formats, transformation, real-time, batch and machine learning processing, scalability, fault tolerance, security and privacy, minimizing the risk of an unsound architecture and technology selection. Big Data Architecture Workshop (BDAW) is a learning event that addresses advanced big data architecture topics. BDAW brings together technical contributors into a group setting to design and architect solutions to a challenging business problem. The workshop addresses big data architecture problems in general, and then applies them to the design of a challenging system. Throughout the highly interactive workshop, students apply concepts to real-world examples resulting in detailed synergistic discussions. The workshop is conducive for students to learn techniques for architecting big data systems, not only from Cloudera?s experience but also from the experiences of fellow students. Workshop Application Use Cases Oz Metropolitan Architectural questions Team activity: Analyze Metroz Application Use Cases Application Vertical Slice Definition Minimizing risk of an unsound architecture Selecting a vertical slice Team activity: Identify an initial vertical slice for Metroz Application Processing Real time, near real time processing Batch processing Data access patterns Delivery and processing guarantees Machine Learning pipelines Team activity: identify delivery and processing patterns in Metroz, characterize response time requirements, identify Machine Learning pipelines Application Data Three V?s of Big Data Data Lifecycle Data Formats Transforming Data Team activity: Metroz Data Requirements Scalable Applications Scale up, scale out, scale to X Determining if an application will scale Poll: scalable airport terminal designs Hadoop and Spark Scalability Team activity: Scaling Metroz Fault Tolerant Distributed Systems Principles Transparency Hardware vs. Software redundancy Tolerating disasters Stateless functional fault tolerance Stateful fault tolerance Replication and group consistency Fault tolerance in Spark and Map Reduce Application tolerance for failures Team activity: Identify Metroz component failures and requirements Security and Privacy Principles Privacy Threats Technologies Team activity: identify threats and security mechanisms in Metroz Deployment Cluster sizing and evolution On-premise vs. Cloud Edge computing Team activity: select deployment for Metroz Technology Selection HDFS HBase Kudu Relational Database Management Systems Map Reduce Spark, including streaming, SparkSQL and SparkML Hive Impala Cloudera Search Data Sets and Formats Team activity: technologies relevant to Metroz Software Architecture Architecture artifacts One platform or multiple, lambda architecture Team activity: produce high level architecture, selected technologies, revisit vertical slice Vertical Slice demonstration Additional course details: Nexus Humans Big Data Architecture Workshop 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 Big Data Architecture Workshop 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 This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam. Overview This course teaches participants the following skills: Position the Professional Data Engineer Certification Provide information, tips, and advice on taking the exam Review the sample case studies Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate Connect candidates to appropriate target learning This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum. Understanding the Professional Data Engineer Certification Position the Professional Data Engineer certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Data Engineer and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case Studies for the Professional Data Engineer Exam Flowlogistic MJTelco Designing and Building (Review and preparation tips) Designing data processing systems Designing flexible data representations Designing data pipelines Designing data processing infrastructure Build and maintain data structures and databases Building and maintaining flexible data representations Building and maintaining pipelines Building and maintaining processing infrastructure Analyzing and Modeling (Review and preparation tips) Analyze data and enable machine learning Analyzing data Machine learning Machine learning model deployment Model business processes for analysis and optimization Mapping business requirements to data representations Optimizing data representations, data infrastructure performance and cost Reliability, Policy, and Security (Review and preparation tips) Design for reliability Performing quality control Assessing, troubleshooting, and improving data representation and data processing infrastructure Recovering data Visualize data and advocate policy Building (or selecting) data visualization and reporting tools Advocating policies and publishing data and reports Design for security and compliance Designing secure data infrastructure and processes Designing for legal compliance Resources and next steps Resources for learning more about designing data processing systems, data structures, and databases Resources for learning more about data analysis, machine learning, business process analysis, and optimization Resources for learning more about data visualization and policy Resources for learning more about reliability design Resources for learning more about business process analysis and optimization Resources for learning more about reliability, policies, security, and compliance Additional course details: Nexus Humans Preparing for the Professional Data Engineer Examination 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 Preparing for the Professional Data Engineer Examination 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 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Duration 3 Days 18 CPD hours This course is intended for Network Security Operations Workload Application Administrators Security Operations Field Engineers Network Engineers Systems Engineers Technical Solutions Architects Cisco Integrators and Partners Overview After taking this course, you should be able to: Define the Cisco telemetry and analytics approach. Explore common scenarios that Cisco Tetration Analytics can solve. Describe how the Cisco Tetration Analytics platform collects telemetry and other context information. Discuss how relative agents are installed and configured. Explore the operational aspects of the Cisco Tetration Analytics platform. Describe the Cisco Tetration Analytics support for application visibility or application insight based on the Application Dependency Mapping (ADM) feature. List the concepts of the intent-based declarative network management automation model. Describe the Cisco Tetration policy enforcement pipeline, components, functions, and implementation of application policy. Describe how to use Cisco Tetration Analytics for workload protection in order to provide a secure infrastructure for business-critical applications and data. Describe Cisco Tetration Analytics platform use cases in the modern heterogeneous, multicloud data center. List the options for the Cisco Tetration Analytics platform enhancements. Explain how to perform the Cisco Tetration Analytics administration. This course teaches how to deploy, use, and operate Cisco© Tetration Analytics? platform for comprehensive workload-protection and application and network insights across a multicloud infrastructure. You will learn how the Cisco Tetration Analytics platform uses streaming telemetry, behavioral analysis, unsupervised machine learning, analytical intelligence, and big data analytics to deliver pervasive visibility, automated intent-based policy, workload protection, and performance management. Exploring Cisco Tetration Data Center Challenges Define and Position Cisco Tetration Cisco Tetration Features Cisco Tetration Architecture Cisco Tetration Deployment Models Cisco Tetration GUI Overview Implementing and Operating Cisco Tetration Explore Data Collection Install the Software Agent Install the Hardware Agent Import Context Data Describe Cisco Tetration Operational Concepts Examining Cisco Tetration ADM and Application Insight Describe Cisco Tetration Application Insight Perform ADM Interpret ADM Results Application Visibility Examining Cisco Tetration Intent-Based Networking Describe Intent-Based Policy Examine Policy Features Implement Policies Enforcing Tetration Policy Pipeline and Compliance Examine Policy Enforcement Implement Application Policy Examine Policy Compliance Verification and Simulation Examining Tetration Security Use Cases Examine Workload Security Attack Prevention Attack Detection Attack Remediation Examining IT Operations Use Cases Key Features and IT Operations Use Cases Performing Operations in Neighborhood App-based Use Cases Examining Platform Enhancement Use Cases Integrations and Advanced Features Third-party Integration Examples Explore Data Platform Capabilities Exploring Cisco Tetration Analytics Administration Examine User Authentication and Authorization Examine Cluster Management Configure Alerts and Syslog Additional course details: Nexus Humans Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) 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 Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) 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 5 Days 30 CPD hours This course is intended for In order to be successful in this class, incoming attendees are required to have current, hands-on experience in developing basic web applications, and be versed in HTML5, CSS3 and JavaScript. This is an intermediate level web development class, designed for experienced web developers, new to Angular, that need to further extend their skills in modern web development. Overview At the end of this five-day course, students will have an application up and running that incorporates components, directives, custom pipes, reactive forms, routes, services, unit testing, and the REST API. They will add authentication, implement the Material library, and learn how to maintain state with NgRX. They will then take a deeper dive including making their own custom directives, lazy loading modules, and E2E testing. They will learn how to enhance their application with animations and create their own Angular library. Working within in an engaging, hands-on learning environment, guided by our expert team, attendees will learn explore: What Angular is and why should you use it How Angular reduces the amount of code that you must write to add rich functionality to both existing and new web pages What TypeScript is, why it is useful, and how to use it with Angular How to facilitate development and deployment using Angular CLI How to work with the various aspects of the Angular architecture to implement clean, responsive web interfaces How Routers can support navigation within a Single Page Application What the best practices are for using Angular so that it works unobtrusively and performs well How to use Angular with HTTP to support JSON, REST, and other services Working with the Ahead of Time compiler including its impact of developers and the development process How to defend against DOM-based XSS How to manage routing decisions based on pre-defined criteria such as a successful authentication How to meet huge data requirements by processing asynchronous data streams with RxJS Simplify server-side rendering How to facilitate unit testing Enhance an Angular user interface with animations and other advanced features Optimize Angular applications with various tools and techniques Maintain state within an Angular application What Angular 9 brings to the table and its relationship to Angular 8 Mastering Angular is a five-day, hands-on course that thoroughly explores the latest Angular features and advances, demonstrating how to solve the traditional challenges of JavaScript web application development. Throughout the course students will build custom components using application routes, form validation, and unit-testing. The course starts with an introduction of Angular CLI and TypeScript, and then delves into component-driven development with Angular components, covering data-binding, directives, services, routing, HTTP, the RxJS library, forms unit testing, and REST. Students will also learn how to add authentication, use the Material library, learn the NgRX design pattern to implement the NgRX store, make custom directives, enhance their application with animations, write an E2E test, and increase their application's efficiency by lazy loading modules and creating their own Angular library Angular Overview Overview of Angular Architecture Getting Started with Angular Getting Started with TypeScript Bootstrapping with Angular CLI Angular Project Structure Working with Angular Components and Events Third Party Libraries Dynamic Views Pipes Angular Forms Forms and the Forms API Single Page Applications and Routes Single Page Applications Services and Dependency Injection Modules Using RESTful Services Overview of REST Angular and REST Angular Best Practices Angular Style Guide What is New in Angular 9 Reactive Programming in Angular Working with RxJS Security and Authentication DomSanitizer JSON Web Tokens Route Guards Enhancing the Angular App Angular Animations Angular Material Angular Elements Deep Dive into Angular Testing and Angular Deep Dive into Components and Directives Deep Dive into Services and Dependency Injection Optimizing for the Enterprise Lazy Loading Optimizing with Universal Creating Your Own Angular Library Maintain State with NgRX NgRX Store Lesson: ES6+ Sass and SCSS for Angular and Material