Skip to main content

Infrastructure as code (IaC)


Infrastructure as code (IaC) is an approach to software development that treats physical compute, storage and network fabric resources as web services and allows apps to run where they are best suited, based on cost and performance data.

Essentially, IaC negates the need for software engineers to be concerned with the physical location of infrastructure components. Instead, when a software application requests infrastructure to run, available services are located through an automated discovery process and resources are allocated on demand. When an infrastructure resource is no longer required, it is re-appropriated so it can be allocated to another application that needs it.

Examples of IaC tools include AWS CloudFormation, Red Hat Ansible, Chef, Puppet, SaltStack and HashiCorp Terraform. Each of these tools has its own way of defining infrastructure, and each allows an administrator to define a service without having to configure a physical infrastructure. These tools are also able to roll back changes to the code, should an unexpected problem arise when new code is released.

Some IaC tools rely on a domain-specific language (DSL), while others use a standard template format, such as YAML and JSON. When selecting an IaC tool, organizations should consider the target deployment. For example, AWS CloudFormation is designed to provision and manage infrastructure on AWS and works well with other AWS offerings. Alternatively, Chef works with on-premises servers and multiple cloud provider IaC offerings.

IaC can be managed through the same version control and automated testing procedures that developers use to maintain quality assurance (QA) in their continuous integration and continuous delivery (CI/CD) pipelines. As of this writing, there are no agreed-upon standards for implementing IaC and the concept is known by several other names, including composable infrastructure, programmable infrastructure and software-defined infrastructure.

Comments

Post a Comment

Popular posts from this blog

Understanding the Evolution: AI, ML, Deep Learning, and Gen AI

In the ever-evolving landscape of artificial intelligence (AI) and machine learning (ML), one of the most intriguing advancements is the emergence of General AI (Gen AI). To grasp its significance, it's essential to first distinguish between these interconnected but distinct technologies. AI, ML, and Deep Learning: The Building Blocks Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Machine Learning, a subset of AI, empowers machines to learn from data and improve over time without explicit programming. Deep Learning, a specialized subset of ML, involves neural networks with many layers (hence "deep"), capable of learning intricate patterns from vast amounts of data. Enter General AI (Gen AI): Unraveling the Next Frontier Unlike traditional AI systems that excel in specific tasks (narrow AI), General AI aims to replicate human cognitive abilities across various domains. I...
Post a Comment
Read more

Normalization of Database

Database Normalisation is a technique of organizing the data in the database. Normalization is a systematic approach of decomposing tables to eliminate data redundancy and undesirable characteristics like Insertion, Update and Deletion Anamolies. It is a multi-step process that puts data into tabular form by removing duplicated data from the relation tables. Normalization is used for mainly two purpose, Eliminating reduntant(useless) data. Ensuring data dependencies make sense i.e data is logically stored. Problem Without Normalization Without Normalization, it becomes difficult to handle and update the database, without facing data loss. Insertion, Updation and Deletion Anamolies are very frequent if Database is not Normalized. To understand these anomalies let us take an example of  Student  table. S_id S_Name S_Address Subject_opted 401 Adam Noida Bio 402 Alex Panipat Maths 403 Stuart Jammu Maths 404 Adam Noida Physics Updation Anamoly :  To upda...
Post a Comment
Read more

How to deal with a toxic working environment

Handling a toxic working environment can be challenging, but there are steps you can take to address the situation and improve your experience at work: Recognize the Signs : Identify the specific behaviors or situations that contribute to the toxicity in your workplace. This could include bullying, harassment, micromanagement, negativity, or lack of support from management. Maintain Boundaries : Set boundaries to protect your mental and emotional well-being. This may involve limiting interactions with toxic individuals, avoiding gossip or negative conversations, and prioritizing self-care outside of work. Seek Support : Reach out to trusted colleagues, friends, or family members for support and advice. Sharing your experiences with others can help you feel less isolated and provide perspective on the situation. Document Incidents : Keep a record of any incidents or behaviors that contribute to the toxic environment, including dates, times, and specific details. This documentation may b...
Post a Comment
Read more