685 PIP courses delivered Online

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: C|ASE-certified professionals Application security professionals DevOps engineers IT security professionals Cybersecurity engineers and analysts Software engineers and testers Anyone with prior knowledge of application security who wants to build a career in DevSecOps Overview This course empowers you with the knowledge and skills to: Understand DevOps security bottlenecks and remediation Understand the DevSecOps toolchain and implement tools Integrate Eclipse and GitHub with Jenkins to build applications Align security practices Integrate threat modeling tools Understand and implement continuous security testing Integrate runtime application self-protection tools Integrate automated security testing Perform continuous vulnerability scans Use AWS and Azure tools to secure applications. Integrate compliance-as-code tools EC-Council Certified DevSecOps Engineer (E|CDE) is a hands-on, instructor-led comprehensive DevSecOps certification program that helps professionals build the essential skills to design, develop, and maintain secure applications and infrastructure. Course Outline Module 1: Understanding DevOps Culture Module 2: Introduction to DevSecOps Module 3: DevSecOps Pipeline?Plan Stage Module 4: DevSecOps Pipeline?Code Stage Module 5: DevSecOps Pipeline?Build and Test Stage Module 6: DevSecOps Pipeline?Release and Deploy Stage Module 7: DevSecOps Pipeline?Operate and Monitor Stage Additional course details: Nexus Humans EC-Council Certified DevSecOps Engineer (E|CDE) 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 EC-Council Certified DevSecOps Engineer (E|CDE) 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 data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a data warehouse analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 3 Days 18 CPD hours This course is intended for The target audience for the DevSecOps Practitioner course are professionals including: Anyone focused on implementing or improving DevSecOps practices in their organization Anyone interested in modern IT leadership and organizational change approaches Business Managers Business Stakeholders Change Agents Consultants DevOps Practitioners IT Directors IT Managers IT Team Leaders Product Owners Scrum Masters Software Engineers Site Reliability Engineers System Integrators Tool Providers Overview After completing this course, students will be able to: Comprehend the underlying principles of DevSecOps Distinguish between the technical elements used across DevSecOps practices Demonstrate how practical maturity concepts can be extended across multiple areas. Implement metric-based assessments tied to your organization. Recognize modern architectural concepts including microservice to monolith transitions. Recognize the various languages and tools used to communicate architectural concepts. Contrast the options used to build a DevSecOps infrastructure through Platform as a Service, Server-less construction, and event-driven mediums Prepare hiring practices to recognize and understand the individual knowledge, skills, and abilities required for mature Dev Identify the various technical requirements tied to the DevSecOps pipelines and how those impact people and process choices. Review various approaches to securing data repositories and pipelines. Analyze how monitoring and observability practices contribute to valuable outcomes. Comprehend how to implement monitoring at key points to contribute to actionable analysis. Evaluate how different experimental structures contribute to the 3rd Way. Identify future trends that may affect DevSecOps The DevSecOps Practitioner course is intended as a follow-on to the DevSecOps Foundation course. The course builds on previous understanding to dive into the technical implementation. The course aims to equip participants with the practices, methods, and tools to engage people across the organization involved in reliability through the use of real-life scenarios and case stories. Upon completion of the course, participants will have tangible takeaways to leverage when back in the office such as implementing DevSecOps practices to their organizational structure, building better pipelines in distributed systems, and having a common technological language. This course positions learners to successfully complete the DevSecOps Practitioner certification exam. DevSecOps Advanced Basics Why Advance Practices? General Awareness People-Finding Them Core Process Technology Overview Understanding Applied Metrics Metric Terms Accelerating People-Reporting and Recording Integrating Process Technology Automation Architecting and Planning for DevSecOps Architecture Basics Finding an Architect Reporting and Recording Environments Process Accelerating Decisions Creating a DevSecOps Infrastructure What is Infrastructure? Equipping the Team Design Challenges Monitoring Infrastructure Establishing a Pipeline Pipelines and Workflows Engineers and Capabilities Continuous Engagement Automate and Identify Observing DevSecOps Outcomes Observability vs. Monitoring Who gets which Report? Setting Observation Points Implementing Observability Practical 3rd Way Applications Revisiting 3rd Way Building Experiments Getting the Most from the Experiment The Future of DevOps Looking Towards the Future Staying Trained Innovation What, and from Who? Post-Class Assignments/Exercises Extended advanced reading associated with Case Stories from the course Additional course details: Nexus Humans DevSecOps Practitioner (DevOps Institute) 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 DevSecOps Practitioner (DevOps Institute) 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 introductory-level course is for experienced application developers new to MongoDB. Overview This course is approximately 50% hands-on lab to lecture ratio, combining engaging expert lessons, demos and group discussions with real-world, skills-focused machine-based labs and exercises. Working in a hands-on learning environment, guided by our expert team, you'll explore: Storage Basics MongoDB Document Model MongoDB Setup CRUD: Basics through Advanced Concepts Performance: Basics through Advanced Concepts Aggregation: Basics through Advanced Concepts Replication: Basics through Advanced Concepts Sharding: Basics through Advanced Concepts Schema Design Security Basics, Authentication & Authorization Application Development and Drivers Geared for experienced developers, Introduction to MongoDB for Developers is a comprehensive course that provides you with hands-on experience with the MongoDB query language, aggregation framework, data modeling, indexes, drivers, basic performance tuning, high availability and scaling. Throughout the course, you?ll explore the MongoDB Atlas database environment in detail, gaining job-ready skills you can put right to work after class. Storage Basics What is a Storage Engine? WiredTiger Storage Engine In-Memory Storage Engine Encrypted Storage Engine MongoDB Document Model JSON and BSON MongoDB Data Types MongoDB Setup Atlas Setup / Local MongoDB Setup CRUD Basics Insert Command Find Command Query Operators Remove Command Updating Documents CRUD Advanced Bulk Writes Retryable Writes Find and Modify Transactions Performance Basics Indexes Aggregation Basics Aggregation Pipeline Concepts Aggregation Pipeline Stages Aggregation Pipeline Expressions Aggregation Advanced $lookup stage $graphLookup stage $expr operator Faceted Search Type Conversions Advanced Expression Operators Date Expression Operators Expression Variables Aggregation Pipeline Optimizations Aggregation in a Sharded Cluster Replication Basics MongoDB Replica Sets Replica Set Use Cases Replication Mechanics Replication Advanced Using Write Concern to Tune Durability Semantics Using Read Concern to Tune Read Isolation Using Read Preference Replica Set Tag Sets Sharding Basics Sharding Concepts When to Shard What is a Shard Key? Zoned Sharding / MongoDB Atlas Global Clusters Sharding Advanced Components of a Sharded Cluster Sharding Mechanics Choosing a Good Shard Key Schema Design Schema Design Core Concepts Common Patterns Security Basics Authentication & Authorization Network Encryption Encryption at Rest Auditing

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 2 Days 12 CPD hours This course is intended for #NAME? Overview The learning objectives for CDA include a practical understanding of: Goals, history, terminology, and pipeline The importance, practices, and transformation of a DevOps collaborative culture Design practices, such as modular design and microservices Continuous Integration (Cl), such as version control, builds, and remediation Tenets and best practices of Continuous Testing (CT) Continuous Delivery and Deployment (CD): packaging, containers, and release Continuous Monitoring (CM): monitoring and analysis infrastructure, process, and apps Infrastructure and tools: frameworks, tools, and infrastructure as code Security Assurance: DevSecOps The opportunity to hear and share real-life scenarios This course is designed for participants who are engaged in the design, implementation, and management of DevOps deployment pipelines and toolchains that support Continuous Integration, Continuous Delivery, Continuous Testing and potentially Continuous Deployment. The course highlights underpinning processes, metrics, APls and cultural considerations with Continuous Delivery. Key benefits of Continuous Delivery will be covered including increased velocity to assist organizations to respond to market changes rapidly, thus being able to outmaneuver competition, reduce risk and lower costs while releasing higher quality solutions. Increased productivity and employee morale by having more activities performed by pipelines instead of humans so teams can focus on vision while pipelines do the execution.This course prepares you for the Continuous Delivery Ecosystem Foundation(CDEF) certification. Course Introduction Course goals Course agenda CDA Concepts Continuous delivery (CD) definition Architecting for continuous delivery Continuous delivery and DevOps Relationships between CD, Waterfall, Agile, ITIL, and DevOps Benefits of continuous delivery CDA Culture Importance of culture to the CD Architect What a CD Architect can do about culture How to maintain culture Assignment: DevOps culture and practices to create flow Design Practices for Continuous Delivery Why design is important to continuous delivery CD Architect?s role in design Key design principles CD best practices Microservices and containers Continuous Integration Continuous integration (CI) defined CD Architect?s role in CI Importance of CI Benefits of CI CI best practices Assignment: Optimizing CI workflows Continuous Testing Continuous testing (CT) defined Importance of CT Benefits of CT CD Architect?s role in CT Five tenets of CT CT best practices Assignment: Handling environment inconsistencies Continuous Delivery and Deployment Continuous delivery defined Continuous deployment defined Benefits of continuous delivery and deployment CD Architect?s role in continuous delivery and deployment Continuous delivery and deployment best practices Assignment: Distinguishing continuous delivery and deployment Continuous Monitoring Continuous monitoring defined Importance of continuous monitoring CD Architect?s role in continuous monitoring Continuous monitoring best practices Assignment: Monitoring build progress Infrastructure and Tools Importance of infrastructure and tools CD Architect?s role in infrastructure and tools Building a DevOps toolchain Infrastructure/tools best practices Assignment: identifying common infrastructure/tool components Security Assurance Importance of security assurance DevSecOps and Rugged DevOps defined CD Architect?s role in security Security best practices Assignment: Applying security practices Capstone exercise Identifying toolchain and workflow improvements Summary Additional Sources of Information Exam Preparations Exam requirements Sample exam review

Duration 2 Days 12 CPD hours This course is intended for The target audience for the DevOps Test Engineering course is anyone involved in defining a DevOps Testing strategy, such as: Delivery Staff DevOps Engineers IT Managers Project Managers Lab Staff Maintenance and Support Staff Quality Assurance Managers Quality Assurance Teams Release Managers Testers Software Engineers Overview The learning objectives for DTE include a practical understanding of: The purpose, benefits, concepts and vocabulary of DevOps testing How DevOps testing differs from other types of testing DevOps testing strategies, test management and results analysis Strategies for selecting test tools and implementing test automation Integration of DevOps testing into Continuous Integration and Continuous Delivery workflows How DevOps testers fit with a DevOps culture, organization and roles This comprehensive course addresses testing in a DevOps environment and covers concepts such as the active use of test automation, testing earlier in the development cycle, and instilling testing skills in developers, quality assurance, security, and operational teams. The course is relevant for every modern IT professional involved in defining or deploying a DevOps testing strategy for their organization, as test engineering is the backbone of DevOps and the primary key for successful DevOps pipeline to support digital transformation. This course prepares you for the Continuous Testing Foundation(CTF) certification. Course Objectives and Modules, Logistics What is DevOps Testing and its Business Benefits?Relation of DevOps Testing in other Test MethodologiesDevOps Testing Best Practices DevOps Testing Terminology Culture changes Organization changes Process and team friction Motivation strategies Measuring Success Continuous Evolution Troubleshooting What is the DevOps pipeline? DevOps Testing on the pipeline Test strategy choices Pre-Flight strategies Continuous Integration Testing System, Delivery and Customer Testing Test Environments Lab Management Topology orchestration Test Automation Frameworks Test Tools Selection criterion Automated metrics Key concepts Test Case Best Practices & Design Exercise Test Suite Best Practices & Design Exercise Principles of DevOps Management DevOps Test Management Metrics DevOps Management Tools DevOps Test Results Analysis Integrating DevOps Results Analysis Test Management Exercise Fictitious Product Test Requirements Individual Exercise Class discussion Exam Preparation

Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Application developers, Cloud Solutions Architects, DevOps Engineers, IT managers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. Overview At the end of the course, students will be able to: Understand container basics. Containerize an existing application. Understand Kubernetes concepts and principles. Deploy applications to Kubernetes using the CLI. Set up a continuous delivery pipeline using Jenkins Learn to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic. Students will also learn how to continuously deploy new code in a Kubernetes cluster to provide application updates. Introduction to Containers and Docker Acquaint yourself with containers, Docker, and the Google Container Registry. Create a container. Package a container using Docker. Store a container image in Google Container Registry. Launch a Docker container. Kubernetes Basics Deploy an application with microservices in a Kubernetes cluster. Provision a complete Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl. Break an application into microservices using Kubernetes? Deployments and Services. Deploying to Kubernetes Create and manage Kubernetes deployments. Create a Kubernetes deployment. Trigger, pause, resume, and rollback updates. Understand and build canary deployments. Continuous Deployment with Jenkins Build a continuous delivery pipeline. Provision Jenkins in your Kubernetes cluster. Create a Jenkins pipeline. Implement a canary deployment using Jenkins. Additional course details: Nexus Humans Getting Started with Google Kubernetes Engine training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Getting Started with Google Kubernetes Engine course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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

Duration 2 Days 12 CPD hours This course is intended for This course is ideal for beginners and intermediate frontend developers who want to become full-stack developers. You will need some prior working knowledge of JavaScript and MongoDB as we skim over its basics and get straight to work. Overview At the end of this day, students should be able to: Understand the MEAN architecture Create RESTful APIs to complete CRUD tasks Build a blogging application with basic features Describe best practices to secure node applications Implement authentication and authorization Create simple animations using Angular Perform unit testing on Angular applications MongoDB, Express, Angular and Node.js Fundamentals begins by demystifying the MEAN architecture. You will review the features of the JavaScript technologies, frameworks, or libraries that make up a MEAN stack. You will also understand how to develop a RESTful API using Node.js, Express.js, and MongoDB Atlas. This course will enable you to discover how to build a blogging application using the MEAN stack. Next, you will learn about authentication using MEAN, and explore the features of Angular, such as pipes, reactive forms, modules and optimizing apps, animations and unit testing, and much more. By the end of the course, you will have all of the knowledge you need to become a pro at developing efficient web applications using JavaScript technologies. Introduction to the MEAN stack MEAN Architecture Demystification Getting Started with Node Activity 1: Creating an HTTP Server for a Blogging Application Understanding Callbacks, Event loop and EventEmitters in Node Understanding Buffers, Streams and Filesystem in Node Activity 2: Streaming Data to a File Developing RESTful APIs to perform CRUD operations Getting Started with RESTful APIs Getting started with MongoDB Atlas Activity 3: Connecting the Node Application with MongoDB Atlas Getting Started with Express Activity 4: Creating Express API Route and Controller Activity 5: Testing Fully Functional RESTful API Beginning Frontend Development with Angular CLI Getting Started with Angular CLI Using Components, Directives, Services, and Making HTTP Requests in Angular Activity 6: Designing the Frontend and Components for the Blogging Application Activity 7: Writing Services and Making HTTP Request Calls to an API Understanding Angular Forms and Routing Activity 8: Creating a Form Application Using the Reactive/Model-Driven Method Activity 9: Creating and Validating Different Forms Using the Template and Reactive Driven Method Activity 10: Implementing a Router for the Blogging Application Understanding MEAN Stack Security Node Security and Best Practices Node Application Authentication with JSON Web Token (JWT) Activity 11: Securing the RESTful API Node Application Authentication with Passport Activity 12: Creating a Login Page to Allow Authentication with Twitter Using Passport Strategies Angular Declarables, Bootstrapping, and Modularity Using Inbuilt Pipes, Custom Pipes, Custom Directives, and Observables Activity 13: Communicating Between Two Components Using Observable Angular Bootstrapping and Modularity Activity 14: Creating a Lazy Loaded Application Testing and Optimizing Angular Applications Angular Animations and Latest Angular Features Activity 15: Animating the Route Transition Between the Blog Post Page and View Post Page of the Blogging Application Optimizing Angular Applications Testing Angular Applications Activity 16: Performing Unit Testing on the App Root Component and Blog-Post Component Overview on the new features in Angular