Post-Graduate Program in Cloud Computing

This section is a pre-work training that teaches you how to work with Python for Cloud and fundamental concepts of Linux.

Python, a high-level, interpreted, object-oriented programming language, is widely used because it has a simple and easy-to-learn syntax, which highlights readability.

Linux is an operating system that is very popular and widely used globally. It supports various devices from mobile phones to supercomputers.

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This section talks about the fundamentals of cloud computing that covers virtualization, delivery and deployment models, IaaS, PaaS, SaaS, and many more.

Virtualization, as the name suggests, is the creation of virtual machines (VM’s), such as servers, desktops, OS, storage devices, networks, and many more.

Containers are standard packages of software that consist of all the necessary elements required to run in any environment.

A cloud service delivery model determines a specific, pre-packaged combination of IT resources that a cloud service provider offers. Its types include IaaS, PaaS, and SaaS.

A cloud deployment model is a particular configuration of environmental parameters such as the accessibility and ownership of the deployment infrastructure and storage size. Its types include Public, Private, Hybrid, and Community Cloud.

This chapter teaches you several critical cloud attributes and services taxonomy.

Infrastructure Automation uses technology that performs tasks with reduced human assistance to control the software, hardware, networking components, OS, and data storage components in order to deliver information technology services and solutions.

IaaS stands for Infrastructure as a Service, a pay-as-you-go service where a third-party vendor offers infrastructure services, like storage and virtualization, via a cloud, through the internet.

PaaS stands for Platform as a Service, a cloud computing service that uses virtualization in order to provide application development platforms to developers or various companies.

SaaS stands for Software as a Service, which is also called a cloud application service. It is the most extensive Cloud Computing service, which delivers an entire application managed by a provider via a web browser.

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Here, you will be introduced to Cloud Computing on AWS, where you will learn Load Balancing, Compute and Storage Services, AWS Organization, and much more.

Compute services are vital resources enabling you to carry out computational abilities through various instructions used by applications and systems. Ex: Amazon Elastic Compute Cloud (Amazon EC2).

Load Balancing is used for automatically distributing incoming application traffic across several targets, like Amazon EC2 instances, IP addresses, containers, Lambda functions, and virtual appliances.

Autoscaling is used to monitor your applications and automatically adjust capacity in order to maintain steady, predictable performance at the least possible expenses. With the help of AWS Auto Scaling, it is easy to set up application scaling for numerous resources across various services in minutes.

AWS offers a wide range of cloud storage services for users to store, access, govern, and analyze their data in order to reduce costs, increase agility, and advance innovation.

Replication empowers automatic, asynchronous copying of objects around Amazon S3 buckets.

Life Cycle Management is used such that objects are stored cost-effectively in every part of their lifecycle.

AWS Organization, an account management service, is used to let you consolidate numerous AWS accounts into an organization, which you can create and centrally manage.

AWS Identity and Access Management (IAM) is used to let you manage access to AWS services and resources securely.

AWS offers the broadest networking services with the highest reliability, security features, and performance globally.

Database Migration Service in AWS aids you in migrating your databases to AWS with virtually no downtime.

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Here, you will learn several managed services on AWS, such as Databases, Firewall, Cloudwatch, Athena, Serverless, and much more.

Amazon RDS, known as Amazon Relational Database Service, is a managed SQL database service by AWS that helps in hardware provisioning, setting up database, migration, patching, backups, and recovery.

Amazon DynamoDB, a NoSQL database service by AWS, lets users have the comforts of autoscaling, in-memory caching, built-in security, backup, and restore options for all the internet-scale applications.

Web Application Firewall in AWS (AWS WAF) protects your web applications or APIs against some common web exploits and bots, which might affect availability, compromise security, or consume enormous resources.

Amazon SNS (Amazon Simple Notification Service), a fully managed messaging service, is used for both application-to-application (A2A) and application-to-person (A2P) communication.

Amazon SQS (Simple Queue Service), a fully managed message queuing service, enables you to decouple and scale microservices, distributed systems, and also serverless applications.

Amazon CloudWatch, a monitoring service in AWS, is developed for DevOps engineers, IT managers and developers, and site reliability engineers (SREs).

Amazon Athena is a serverless and interactive query service, making it easy to analyze data in Amazon S3 (cloud storage service) with the help of standard SQL.

Amazon QuickSight is a serverless, scalable, embeddable, machine learning-powered business intelligence (BI) service, which is built for the cloud and allows you to easily create and publish interactive BI dashboards, including machine learning-powered insights.

Amazon Kinesis lets you ingest, buffer, and process streaming data in real-time so that you can derive insights in mere seconds or minutes rather than hours or days.

AWS Lambda, a serverless compute service, allows you to run code without provisioning or managing servers.

Amazon Rekognition, an AWS service, makes it pretty simple to add image analysis to your applications.

Amazon Polly, an AWS service, helps in turning text into lifelike speech, which lets you create applications that talk and build wholly new categories of speech-enabled products.

Amazon Comprehend is a Natural Language Processing (NLP) service, which makes use of machine learning to reveal information in unstructured data.

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In this next section, you will learn how to work with Containers and AWS DevOps, including topics like Docker, ECS, Deployment Pipeline, CloudFormation, and Terraform.

Docker, an open and lightweight container, is used to develop, ship, and run applications.

ECS is a fully managed container orchestration service that quickly enables the deployment, management, and scalability of containerized applications.

AWS CodeCommit is a highly scalable, secure, managed source control service, which hosts private Git repositories.

AWS CodeDeploy, a fully managed deployment service, helps you automate software deployments to various compute services.

AWS CodePipeline, a fully managed continuous delivery service, automates your release pipelines for quick and reliable application and infrastructure updates.

CloudFormation is a service that lets you model and set up your AWS resources, so you can spend less time managing AWS resources and more time focusing on your AWS applications.

Terraform, similar to Cloud Formation, is an “Infrastructure as Code (IaC)” tool that enables you to create, update, and version your AWS infrastructure.

Moving ahead, you will learn enterprise cloud solutions, including topics such as data architecture, data streaming, setting up a cloud-based development environment, and much more.

Here, you will learn about various concepts within data architecture and the serverless approach.

Here, you will learn to set up a cloud-based development environment with a demo.

This chapter teaches you the basics of data streaming and data analytics on the cloud.

This chapter teaches you the procedure to set up a Kinesis data stream, which is a hugely scalable and durable real-time data streaming service.

Here, you will learn how to deploy an application to Elastic Beanstalk, an example of Platform as a Service (PaaS).

AWS Step Functions, a low-code visual workflow service, is used for organizing AWS services, automating business processes, and building serverless applications.

Elasticsearch is an open-source search and analytics engine for use cases like log analytics, real-time application monitoring, and clickstream analytics.

In this section of AWS specialization, you will learn about Solutions Architect roles, like Networking, Manages Services, and Security.

AWS Networking Services allow users to separate their cloud infrastructure, accelerate workload requests, and connect the physical network to personal private virtual networks. Examples include Amazon VPC, Amazon CloudFront, Elastic Load Balancing, and many more.

AWS Managed Services (AMS) allows users to operate their AWS infrastructure more efficiently and securely.

AWS Cloud Security Services aid in protecting your data, accounts, and workloads from unauthorized access. Examples include AWS IAM, AWS Secrets Manager, and much more.

In the last section of AWS specialization, you will learn about Application Developer roles, like Architecture, DevOps, Database, and Gaming.

Here, you will learn all the concepts of AWS architecture and DevOps.

Here, you will learn about various cloud platforms in AWS.

This chapter teaches you about the handling of data and databases in AWS.

This chapter covers various AWS applications in web, mobile, and gaming.

The first section of this Azure introduction will teach you all the fundamentals of Azure and its numerous services.

Like AWS, Azure is another cloud service developed by Microsoft. Here, you will learn about various Azure services.

Azure Virtual Machines (VMs) are several on-demand, scalable computing resources offered by Azure. They give you the flexibility of virtualization without the need to buy and maintain the physical hardware.

Azure Blob (Binary Large Object) Storage aids you in creating data lakes for your analytical requirements and offers storage to build powerful cloud-native and mobile applications.

Azure Files provides fully managed file shares in the cloud that are accessible through the industry-standard Server Message Block (SMB) protocol or Network File System (NFS) protocol.

An Azure Virtual Network (VNet) is a representation of one’s own network in the cloud. VNet is a logical isolation of the Azure cloud, which is dedicated to your subscription.

Azure Active Directory (Azure AD) is a cloud-based identity and access management service (Microsoft’s IAM), which lets users sign in and access resources from various internal and external resources.

Here, you will gain an understanding of how to control the cost management of Azure services via alerts, budgeting, and pricing calculators.

This is Azure’s deployment and management service, which provides a management layer that lets you create, update, and delete resources in your Azure account.

In the next section, you will learn about Azure Security Solutions, covering Load Balancing, Network Security, and Security & Governance Solutions.

Load Balancing is referred to evenly distributing load, i.e., incoming network traffic, across a group of backend servers or resources.

Here, you will learn about load balancers in Azure, such as Azure Load Balancer, Application Gateway, WAF, and Azure Traffic Manager.

Network Security is the method to protect resources from unauthorized access or any attacks by applying controls to network traffic.

Here, you will be briefed about an overview of Azure Network Security, such as Network Security Groups, Azure Bastion, and Firewall.

Here, you will learn various other security and governance solutions in Azure.

Moving forward, this module will teach all the available Azure solutions for apps, such as Azure Functions, API management, Azure DevOps, Messaging, Event-based, and Monitoring services.

Azure App Services allows you to create apps faster with a unique cloud service in order to quickly and easily create enterprise-ready web and mobile apps for any device or platform and deploy them on a reliable and scalable cloud infrastructure.

API Management in Azure is a fully managed service that lets customers publish, transform, maintain, monitor, and secure APIs.

Here, you will learn the process of using end-to-end developer and CI/CD tools for developing, updating, and deploying your containerized applications.

Here, you will learn various messaging and event-based services on Azure, such as Service Bus, Event Hub, and Event Grid.

Azure DevOps offers developer services for support teams in order to plan work, collaborate on the development of code, and build and deploy applications.

Monitoring services in Azure let you monitor the performance and availability of your Azure environment, which provides you with insights and telemetry into your cloud workloads.

Followed by Azure Solutions, you will then learn about Azure Data Platforms that cover topics such as Cosmos DB, SQL Database, and Data Services Intro.

Azure Cosmos DB is an extensively managed NoSQL database that is used for modern app development. It can take care of automatic management, updates, and patching.

An SQL (Structured Query Language) database is a collection of tables storing a specific set of structured data. Here, you will learn more about SQL database options and models.

Here, you will be introduced to various data services in Azure, such as Azure Data Factory, Synapse, Databricks, and Stream Analytics.

Azure ML Services is a self-paced module, which will cover topics like Azure ML Studio, Data Manipulation, Visualizing categorical and numerical data, and many more.

Here, you will grasp some knowledge on how to create and manage Machine Learning solutions for Azure in order to help Machine Learning Engineers or Data Scientists leverage their existing data processing skills and model development skills and frameworks.

Here, you will learn how to implement various Azure AI services, which will aid in modernizing business processes, accelerating development, improving customer experience, and much more.

Azure ML Studio is a web portal or workspace for Data Scientists or Machine Learning Engineers, where they create, build, train various Machine Learning models.

Here, you will learn how to load data, like updating data, training Machine Learning models, etc., using Azure Machine Learning resources.

This topic will teach you how to manipulate data in Azure, like combining two datasets by using joins, modifying column headings and data types, and much more.

Here, you will learn how to visualize categorical data using Azure ML services.

Here, you will learn how to visualize numerical data, along with other kinds of data, using Azure ML services.

Microservices, also called Microservices architecture, is an architectural approach that organizes or structures applications as a collection of services, which are loosely coupled, highly maintainable and testable, and independently deployable, owned by a small number of people or a team.

This topic will teach you about the characteristics of Microservices and what you can expect from them.

Here, you will learn about various challenges faced in modelling, its failures, and also about load balancing.

The 12 Factor App resolves the boundary between application and infrastructure, bridges the gap between development and production, and facilitates your microservices to scale up or down without any significant changes to tooling, architecture, or development practices.

This topic will teach you how to handle Microservices Architecture and Design Patterns in order to reduce costs, increase productivity, improve resilience, and many more.

Anti Patterns are typical responses to a recurring problem that is generally ineffective and risks being highly counterproductive.

An Event-Driven architecture uses events in order to trigger and communicate between decoupled services, which is common in modern applications that are built with Microservices.

Here, you will learn the process of securing your Microservices architecture using the best practices.

Here, you will learn the process of migrating the existing monolithic applications to microservices.

Big Data Analytics is the method of using advanced analytical techniques on large and complex data sets - structured, semi-structured, and unstructured, from several resources.

NoSQL databases, also called "not only SQL", are used to store data differently in a non-tabular format instead of relational tables. Examples of NoSQL databases in Big Data Analytics include Cassandra and DynamoDB.

Cassandra, developed by Apache, is an open-source NoSQL distributed database that provides high scalability and availability by handling massive amounts of data across numerous servers without compromising performance.

DynamoDB, developed by Amazon, is a NoSQL database service that lets users have the comforts of autoscaling, in-memory caching, built-in security, backup, and restore options for all the internet-scale applications.

A partitioner, as the name suggests, partitions the key-value pairs of intermediate Map-outputs.

Replication, as the name implies, creates one or more redundant copies of a database or other data store with the intention of fault tolerance.

A snitch is used to determine which data centers and racks nodes belong to.

Here, you will learn how to work with Query Driven Design and Distributed Development.

Amazon EMR (Amazon Elastic MapReduce) is the leading cloud and big data platform in the industry, which is used for processing massive amounts of data using open source tools, like Apache Hive, Apache Spark, Apache HBase, and many more.

Apache Hadoop is an open-source framework that enables the distributed processing of enormous data sets across clusters of computers with the help of simple programming models.

Apache Hive is an SQL-based, open-source data warehouse that enables reading, writing, and managing large datasets that are stored in other data storage systems.

Cloud Security services aid in protecting your data, accounts, and workloads from unauthorized access. Cloud migration aids in migrating your existing applications and infrastructure to the cloud.

AWS Organization, an account management service, is used to let you consolidate numerous AWS accounts into an organization, which you create and centrally manage.

This topic will teach several cloud security services in AWS, such as Trusted Advisor, Data encryption, Amazon Inspector, AWS Config, Guard Duty, and Macie.

Identity and access control is the authority to manage access to enterprise resources in order to keep systems and data secure.

AWS Cognito, a simple user identity and data synchronization service, aids you in securely managing and synchronizing app data for your users across their mobile devices.

Cost Explorer in AWS has an easy-to-use interface, which allows you to visualize, understand, and manage your AWS costs and usage over time.

Here, you will learn the best practices and strategies for successfully migrating your infrastructure to the cloud.

Here, you will learn about the Migration Maturity Index for the cloud.

API-based integration enables you to establish how data transfers between applications and under what circumstances.

This topic will teach you how to study workload analysis of cloud computing.

The services and infrastructure in a private cloud are maintained on a private network on all occasions, where software and hardware resources are committed fully to your organization or business.

OpenStack is a cloud operating system, which controls enormous amounts of compute, storage, and networking resources via a data center. They are managed and provisioned through APIs with standard authentication procedures.

Like AWS and Microsoft Azure, Google Cloud Platform (GCP) is another cloud service developed by Google.

Here, you will be briefed about the overview of Regions, Resources, and Services in GCP.

Google Compute Engine provides machine-type recommendations in order to let you optimize the resource utilization of your virtual machine (VM) instances.

Here, you will be introduced to Google Compute Engine, along with several other essential topics, like Instance Groups, Load Balancing, and Autoscaling.

In this topic, you will be taught various storage and networking services in GCP.

Cloud SDK is a set of tools and libraries that can be used to manage resources and applications hosted on GCP. These tools are: gcloud, gsutil, and bq command-line tools.

Google App Engine is an application hosting and development platform, which aids in accelerating everything from enterprise web applications to mobile games, with the help of the same infrastructure that accelerates global-scale web applications of Google.

Google Kubernetes Engine (GKE) offers a managed environment to deploy, manage, and scale your containerized applications using Google infrastructure.