Setting up DevOps Pipeline on Amazon Web Services (AWS)

What is Cloud Computing?

In simple language, Cloud computing is allowing users or systems to access the application and services outside private networks of organizations other than those who are authorized to access them. Like we can access applications and services on the internet instead of being able to use only those which we have on our hard drive. The customers or business group who want to use these applications and services they need to pay for it. Its infrastructure is controlled by the cloud provider, not by particular cloud customers. Features of Cloud Computing -

Resources are easily available.
An extensive network can be accessed within minutes.
Very Economical.
Services on demand.
Extremely Secure.
Maintenance is mere.

Cloud computing plays a significant role in making the best possible choices for IoT devices. Click to explore about, Edge Computing vs Cloud Computing?

What are the benefits of Cloud Computing?

Like a customer wants the access of data centers and servers so, with the help of AWS cloud, customer can only pay for those services which they want to consume instead of investing heavily on unrequired data centers and servers. It eliminates the issue of capacity guessing. E.g., before deploying the application, if the organization has taken any capacity, then most of them face the point where they end up having limited ability or the precious resources are left idle.

So, with the help of AWS cloud, you can increase or decrease the capacity as per your access requirement in just few minutes. As data centers and services can be accessed very easily, so it ends up increasing the speed and agility for any organization. It also allows the organizations to only focus on services they have taken, which removes unnecessary expenditure on maintaining bought data centers.

What are the types of Cloud Computing?

Infrastructure as a service (IaaS) - Contains cloud IT and provides space for storage of data, networking features, and computer (virtual/dedicated hardware). It offers the best flexibility and management discipline over IT resources and is very familiar to the IT resources which most IT departments and developers use today.
Service (PaaS) - Eliminates the need to take care of infrastructure, capacity planning, maintenance of software, etc which eventually leads to more efficiency.
Software as a Service (SaaS) - Means to use the completed product without taking care of infrastructure. The service provider maintains the provided product. For e.g., Gmail.

The conveyance of processing administrations—including capacity, servers, databases, examination, systems administration, programming, and insight on the Internet. Click to explore about, CaaS vs PaaS - Which is a better Solution?

What is a Data Pipeline?

A pipeline is also called a data pipeline. In the pipeline, there are a set of data elements that are processed, and these are connected in series, where the output of the current item is the input of next element. All the elements in the pipeline are most often executed parallel. It removes the delay between the instructions that have already been executed because, with the help of it, we can process more instructions simultaneously. Types of a pipeline

Instruction pipelines
Graphic pipelines
Software pipelines
HTTP pipelining

What is AWS DevOps Pipeline?

It is the latest method of creating and deploying software, plus it has brought a cultural shift in companies as well, which has led to the introduction of an agile approach in the software development life cycle. There are different tools used to achieve this process. Ideally, the involvement of humans is very little in the pipeline. The code is taken in one end and is deployed on other ends after going through different processes. The tools fall into below-mentioned categories -

Source Code control
Build a tool
Containerisation
Configuration Management
Monitoring

There is a term ChatOps as well, which is referred to as conversation-driven development. Its tools are HipChat or Slack.

Source Code Control for DevOps Pipeline AWS

It is an essential part of the pipeline, as this is the place where the codes are submitted and tested into the pipeline by the developers. Everything which is needed to build, test, and deploy is stored in source repositories. Once a new code is added to the repository, this sets off build process, which is the next step of the pipeline. This is most often done with Git server, it creates the push notification and tells the build machine to start.

DevOps Pipeline AWS Build Tools

Two types of tools are grouped here

Local build tools like Maven (file - .Xml), Gradle (used by companies like Netflix, Google and LinkedIn), sbt (file - .sbt) or npm.
Server-based solutions like Jenkins, Travis CI, or bamboo.
Get a copy of code
Get all dependencies
Compile
Tests
Package

As the project is built, the pipeline gets split. The code is directly deployed from the build server by some of the companies. Some send a notification to fetch the deploy code and deploy it, and some send it to another block called containerization.

What is Containerization?

These are containers in the form of boxes containing code and rest part on which code relies to run without errors. Any operating system can load and unload them. The most popular tool is Docker. It builds a box from a script which is called as Dockerlife. This script is only a text file so that it can be stored beside the code in source control. Containers can run on any system, and they are wholly infrastructural.

This means we can build the box on the Windows machine, share it with a Linux machine in Cloud, and it will still run on both the systems that, too, in the same way. All files, libraries, and also the operating systems that container needs are already inside it. They should contact me as small as possible so that we can start or transfer them between the machines very quickly and frequently. No data should be shared in containers/boxes because they can be started, stopped, and removed at will. Then there is another split, meaning we can either deploy the containers directly or can pass it over to configuration management tools.

The processing as an appropriated worldview. It brings information about data and registers power nearer to the gadget or information source where it's generally required. Click to explore about, Difference Between Cloud, Edge, and Fog Computing?

Configuration Management Tool for Building DevOps Pipeline AWS

Configuration management is mainly ensuring that servers are working as expected. The essential tools involved here are Chef, Puppet, or Ansible. It is generally a master server as it holds the configuration for every agent. There are two styles, namely, push and pull. Push style system generally informs every agent to check if there is any need to update or is the current configuration in resemblance to what is required. Pull style notification is operated on Chef and Puppet. It informs the agent to check if there are any changes, and this process is executed in every half an hour.

Again, all the configurations are held in a series. There are modules also which deal with the concept of Infrastructure as code. This means that the number of servers and their networking is also entitled in terms of files. Different systems use different clouds like AWS uses CloudFormation. Azure uses resource management templates.

Monitoring Tools for Developing DevOps Pipeline AWS

It is key to a successful pipeline. Any change can be made to infrastructure via updating the text files. The monitoring tool helps in checking whether the change made was beneficial or not. It can also inform you whether the machine is overloaded with requests or not, and if there is traffic, then to calm it down, it will instruct the configuration management tool to create more machines and add these to the load balancer.

If there is any problem monitoring system will notice it before the user experiences it and will remove it by sending the notification to people who are running the pipeline at that time or automated system can also kick in. This pipeline does not cover everything as security team is not part of it so, to make it more efficient more and more, security checks should also be added in between like there are a lot of tools available to do standard tests so these should also be integrated within it before anything is pushed forward to deployment. Automated security checks can also be added in the pipeline.Continuous Integration and Continuous Delivery also plays a very vital role in the DevOps.

AWS DevOps Pipeline Implementation

The below content show the implementations of DevOps Pipeline on AWS

Role of Continuous Integration

Mostly refers to the integration stage or build stage of the software release process requiring both a cultural component and an automation component (e.g., build service). The key goals are to quickly address and find bugs, improve software quality, and decrease the validation time and release new software updates. It generally involves four stages -

Source control – commit changes (automated) new york time now
Build – Run Build and unit tests (automated)
Staging – Deploy to test environment (run integration tests, load tests, and other tests)
Production – Then, deploy to a production environment.

Implementing Continuous Delivery

Continuous Delivery involves a process in which code changes are prepared for a release to production automatically. It also allows developers to automate testing further beyond unit tests so they can verify application updates on multiple dimensions as well before deploying code to customers. Some of the tests are UI testing, integration testing, load testing, API reliability testing, etc.

Continuous Delivery vs. Continuous Deployment

The main difference is that continuous deployment production happens automatically, whereas, in continuous delivery, full production needs to be done manually, which means it requires approval.

Cloud computing is using remote servers on the internet to manage, store, and process data instead of using your personal computer or a local server. Click to explore about, Microsoft Azure DevOps Build Pipeline

Building Amazon Web Services (AWS) DevOps Pipeline

It is a subsidiary company of Amazon which provides cloud computing services or platforms on-demand to companies, individuals and governments. It provides a combination of IaaS, PaaS and SaaS offerings. AWS portfolio comprises more than 100 services involving those for compute, database, infrastructure management, application development, and security, etc. Some of these services include -

Amazon EC2

Amazon Elastic Compute Cloud (Amazon EC2) issues virtual servers called instances- for compute capacity. The EC2 service provides a lot of instance types with varying sizes and capacities.

Amazon S3

Amazon Simple Storage Service (Amazon S3) issues scalable object storage for archival, analytics and data backup. There is one more named Amazon Elastic Block Store, which provides block-level storage volumes for determined data storage for use with EC2 instances.

Amazon Relational Database Services

Amazon Relational Database Services provides manageable database services. Amazon Redshift offers a data warehouse that helps data analysts in performing BI tasks more efficiently.

Amazon Virtual Private Cloud Networking

An Amazon Virtual Private Cloud (VPC) allows a full administrator control over a virtual network so that isolated sections of the AWS cloud can also be used. Network traffic can be balanced with the help of the Network Load Balancer and Application Load Balancer by the admin.

Development tools and application services

To deploy and manage applications and services, a developer is provided with AWS command-line tools and software development kits(SDKs). CI and CD pipelines can be created by a developer with services like AWS CodeBuild, AWS CodePipeline, and CodeDeploy.

AWS CodePipeline

It is a wholly manageable continuous delivery service that helps in automating the release pipelines for rapid and reliable infrastructure and application updates. It automates the building, testing, and deploying phases of the release process every time the code gets changed, and this is based on a release model defined by you. We can easily integrate AWS CodePipeline also with third-party services like GitHub etc. We only pay for what we use.

AWS DataPipeline

It is a web service that assists you to reliably move and process data among different AWS storage and compute services, additionally on-premises data sources, at specified intervals. With AWS Data Pipeline, you can access data at the stored place, data can be transformed as well as processed, and the results can be efficiently transferred to AWS services such as Amazon S3, etc. This Data Pipeline is reliable, easy to use, flexible, scalable, and low cost.

A Comprehensive Approach

With the help of the AWS cloud, applications can be deployed around the world in some clicks. To learn more about DevOps Processes we recommend taking the following steps