02 terraform core concepts

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Terraform is an Infrastructure Automation tools. This can work equally good for on-premises, public cloud, private cloud, hybrid-cloud and multi-cloud infrastructure. Visit us for more at www.zekeLabs.com

Terraform:
Core Concepts
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Architecture of Terraform
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Core concepts in terraform configuration
● Providers: A source of resources. [With an API endpoint and authentication. E.g AWS]
● Resource: Everything that has a set of configurable attributes and a lifecycle such as create,
read, update, delete. [aws ec2 instance] -- impies id and state
● Data : information read from providers. E.g. lookup from own AWS account for ami_id or
keypairs.
● Provisioner: initialize a resource from a local or remote script.
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Providers
● A provider is responsible for understanding API interactions and exposing resources.
● Providers generally are :
○ IaaS (e.g. AWS, GCP, Microsoft Azure, OpenStack),
○ PaaS (e.g. Heroku),
○ SaaS services (e.g. Terraform Enterprise, DNSimple, CloudFlare).
● Providers define resources and data.
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Resources
● Basic building block of terraform scripts.
● Terraform is used to create, manage, and update infrastructure resources such as physical
machines, VMs, network switches, containers, and more.
● Almost any infrastructure type can be represented as a resource in Terraform.
● Resources undergo CRUD operation.
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Data
● Data sources allow data to be fetched or computed for use elsewhere in Terraform
configuration.
● A data source may retrieve artifact information from
○ Terraform Enterprise,
○ Pre-existing resources from Providers,
■ E.g. look up a list of AMIs available in your AWS account.
● As data sources are essentially a read only subset of resources
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Terraform State
● Terraform State stores status of your managed infrastructure and configuration.
● This state is used by Terraform to map real world resources to your configuration, keep track
of metadata, and to improve performance for large infrastructures.
● State is stored by default in a local file named "terraform.tfstate", but it can also be stored
remotely, which works better in a team environment (backends).
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Provisioners
● Provisioners are added directly to any resource:
● Used to :
○ execute scripts on a local or remote machine as part of resource creation or destruction.
○ Used to bootstrap a resource,
○ cleanup before destroy,
○ run configuration management, etc.
● Example:
○ chef
○ file
○ habitat
○ Local-exec and remote-exec
○ null_resource
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$output ● Shows values that are highlighted after terraform applies. ● Can be seen by running command: terraform output ● Maintained in terraform state output "ami_id" { value = "${aws_instance.web.ami_id}" } info@zekeLabs.com | www.zekeLabs.com | +91 8095465880$

Components of output
● Multiple output variables can be configured with multiple output blocks.
● Parameter:
○ Value (string, list, map)
○ Description
○ Depends_on (list) # dependencies will be created before this output value is processed
○ Sensitive (boolean) # confidential, value is not revealed, can be seen by “terraform output”
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modules
● Modules -
○ encapsulate groups of resources in your infrastructure.
○ are subdirectories with self-contained terraform code
○ may be sourced from Git, Mercurial, HTTPS locations
○ use variables and outputs to pass data
● All attributes within the block must correspond to variables within the module, with exception
of :
○ Source
○ Version
○ provider
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Terraform WorkFlow
● Write - Author infrastructure as code.
● Plan - Preview changes before applying.
● Apply - Provision reproducible infrastructure.
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Configuration
● Terraform format ends in :
● .tf
● .tf.json
● Multiple file formats can be present in the same directory.
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Lab: Writing first Terraform script
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Agenda 1. The changing landscape of IT Infrastructure 2. Containers - An introduction 3. Container management systems 4. Kubernetes 5. Containers and DevOps 6. Future of Infrastructure Mgmt About the talk In this talk, you will get a review of the components & the benefits of Container technologies - Docker & Kubernetes. The talk focuses on making the solution platform-independent. It gives an insight into Docker and Kubernetes for consistent and reliable Deployment. We talk about how the containers fit and improve your DevOps ecosystem and how to get started with containerization. Learn new deployment approach to effectively use your infrastructure resources to minimize the overall cost.

Docker - A curtain raiser to the Container worldzekeLabs Technologies

The slides talk about Docker and container terminologies but will also be able to see the big picture of where & how it fits into your current project/domain. Topics that are covered: 1. What is Docker Technology? 2. Why Docker/Containers are important for your company? 3. What are its various features and use cases? 4. How to get started with Docker containers. 5. Case studies from various domains

Serverless and cloud computingzekeLabs Technologies

SQLzekeLabs Technologies

1. The document provides information on database concepts like the system development life cycle, data modeling, relational database management systems, and creating and managing database tables in Oracle. 2. It discusses how to create tables, add, modify and delete columns, add comments, define constraints, create views, and perform data manipulation operations like insert, update, delete in Oracle. 3. Examples are provided for SQL statements like CREATE TABLE, ALTER TABLE, DROP TABLE, CREATE VIEW, INSERT, UPDATE, DELETE.

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Nearest neighborszekeLabs Technologies

This document provides an overview and agenda for a presentation on nearest neighbors algorithms. It will cover fundamentals of nearest neighbors, using nearest neighbors for unsupervised learning, classification, and regression. Specific topics that will be discussed include k-nearest neighbors algorithms, algorithms to store training data like brute force and k-d trees, nearest neighbors classification using k-nearest neighbors and radius-based classifiers, nearest neighbors regression, and the nearest centroid classifier.

Naive bayeszekeLabs Technologies

This document provides an overview of Naive Bayes classification. It begins with an introduction to Bayes' theorem and how it can be used to calculate conditional probabilities. It then discusses the key assumptions of Naive Bayes that predictors are independent of each other. Finally, it outlines the different types of Naive Bayes models including Gaussian, Multinomial, and Bernoulli and provides a thank you and call to action at the end.

Master guide to become a data scientist zekeLabs Technologies

This document outlines a 20 module, 50 hour course from zekeLabs to become a data scientist. The course covers topics like numerical computation with NumPy, essential statistics, machine learning algorithms like linear regression, logistic regression, naive bayes, trees, and ensemble methods. It also discusses model evaluation, feature engineering, deployment and scaling. The document provides details on the topics covered in each module and contact information for the course.

Linear regressionzekeLabs Technologies

This document provides an overview of linear regression techniques. It begins with introducing deterministic vs statistical relationships and simple linear regression. It then covers model evaluation, gradient descent, and polynomial regression. The document discusses bias-variance tradeoff and various regularization techniques like lasso, ridge regression and stochastic gradient descent. It concludes with discussing robust regressors that are robust to outliers in the data.

Linear models of classificationzekeLabs Technologies

This document discusses linear models for classification. It outlines an agenda covering logistic regression, its limitations for multi-class classification problems and predicting unstable boundaries with limited data. It also mentions the need for linear discriminant analysis and addressing bias-variance tradeoffs, errors, and multicollinearity which can impact models. The document provides context and an overview of key topics for working with linear classification models.

Grid search, pipeline, featureunionzekeLabs Technologies

This document discusses pipelines and feature unions in scikit-learn. It explains that pipelines allow connecting estimators and transformers sequentially to build models. Transformers preprocess data while estimators perform the learning. Grid search can tune hyperparameters across all pipeline steps. Feature unions concatenate results of multiple transformers. Pipelines integrate well with grid search and provide modularity while feature unions combine different feature extraction methods. The limitations are that pipelines do not support partial fitting.

Feature selectionzekeLabs Technologies

This document discusses feature selection for machine learning models. It outlines the goal of becoming a data scientist and creating a plan to achieve that goal. It then discusses some limitations of logistic regression models for classification tasks, including that they are best for binary rather than multi-class classification, can predict unstable decision boundaries when classes are well separated, and can be unstable predictors with limited training data. It also provides a link to a resource on understanding variance.

Essential NumPyzekeLabs Technologies

This document provides an overview of NumPy, an open source Python library for numerical computing and data analysis. It introduces NumPy and its key features like N-dimensional arrays for fast mathematical calculations. It then covers various NumPy concepts and functions including initialization and creation of NumPy arrays, accessing and modifying arrays, concatenation, splitting, reshaping, adding dimensions, common utility functions, and broadcasting. The document aims to simplify learning of these essential NumPy concepts.

Ensemble methods zekeLabs Technologies

Ensemble methods combine multiple machine learning models to obtain better predictive performance than could be obtained from any of the constituent models alone. The document discusses major families of ensemble methods including bagging, boosting, and voting. It provides examples like random forest, AdaBoost, gradient tree boosting, and XGBoost which build ensembles of decision trees. Ensemble methods help reduce variance and prevent overfitting compared to single models.

Dimentionality reductionzekeLabs Technologies

The document provides an overview of dimensionality reduction techniques, including PCA, SVD, and LDA. PCA uses linear projections to reduce dimensions while preserving variance in the data. It computes eigenvectors of the covariance matrix. SVD is similar to PCA but works directly with the data matrix rather than the covariance matrix. LDA aims to maximize class separability during dimensionality reduction for classification tasks. It computes within-class and between-class scatter matrices. While PCA maximizes variance, LDA maximizes class discrimination.

Data PreprocessingzekeLabs Technologies

This document discusses data preprocessing techniques for machine learning. It covers common preprocessing steps like normalization, encoding categorical features, and handling outliers. Normalization techniques like StandardScaler, MinMaxScaler and RobustScaler are described. Label encoding and one-hot encoding are covered for processing categorical variables. The document also discusses polynomial features, custom transformations, and preprocessing text and image data. The goal of preprocessing is to prepare data so it can be better consumed by machine learning algorithms.

Webinar - Build Cloud-native platform using Docker, Kubernetes, Prometheus, I...zekeLabs Technologies

Design Patterns for Pods and Containers in Kubernetes - Webinar by zekeLabszekeLabs Technologies

[Webinar] Following the Agile Footprint - zekeLabszekeLabs Technologies

Machine learning at scale - Webinar By zekeLabszekeLabs Technologies

A curtain-raiser to the container world Docker & KuberneteszekeLabs Technologies

Docker - A curtain raiser to the Container worldzekeLabs Technologies

Serverless and cloud computingzekeLabs Technologies

SQLzekeLabs Technologies

Outlier detection handlingzekeLabs Technologies

Nearest neighborszekeLabs Technologies

Naive bayeszekeLabs Technologies

Master guide to become a data scientist zekeLabs Technologies

Linear regressionzekeLabs Technologies

Linear models of classificationzekeLabs Technologies

Grid search, pipeline, featureunionzekeLabs Technologies

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02 terraform core concepts

1. Terraform: Core Concepts info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

2. Architecture of Terraform info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

3. Core concepts in terraform configuration ● Providers: A source of resources. [With an API endpoint and authentication. E.g AWS] ● Resource: Everything that has a set of configurable attributes and a lifecycle such as create, read, update, delete. [aws ec2 instance] -- impies id and state ● Data : information read from providers. E.g. lookup from own AWS account for ami_id or keypairs. ● Provisioner: initialize a resource from a local or remote script. info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

4. Providers ● A provider is responsible for understanding API interactions and exposing resources. ● Providers generally are : ○ IaaS (e.g. AWS, GCP, Microsoft Azure, OpenStack), ○ PaaS (e.g. Heroku), ○ SaaS services (e.g. Terraform Enterprise, DNSimple, CloudFlare). ● Providers define resources and data. info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

5. Resources ● Basic building block of terraform scripts. ● Terraform is used to create, manage, and update infrastructure resources such as physical machines, VMs, network switches, containers, and more. ● Almost any infrastructure type can be represented as a resource in Terraform. ● Resources undergo CRUD operation. info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

6. Data ● Data sources allow data to be fetched or computed for use elsewhere in Terraform configuration. ● A data source may retrieve artifact information from ○ Terraform Enterprise, ○ Pre-existing resources from Providers, ■ E.g. look up a list of AMIs available in your AWS account. ● As data sources are essentially a read only subset of resources info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

7. Terraform State ● Terraform State stores status of your managed infrastructure and configuration. ● This state is used by Terraform to map real world resources to your configuration, keep track of metadata, and to improve performance for large infrastructures. ● State is stored by default in a local file named "terraform.tfstate", but it can also be stored remotely, which works better in a team environment (backends). info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

8. Provisioners ● Provisioners are added directly to any resource: ● Used to : ○ execute scripts on a local or remote machine as part of resource creation or destruction. ○ Used to bootstrap a resource, ○ cleanup before destroy, ○ run configuration management, etc. ● Example: ○ chef ○ file ○ habitat ○ Local-exec and remote-exec ○ null_resource info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

9. output ● Shows values that are highlighted after terraform applies. ● Can be seen by running command: terraform output ● Maintained in terraform state output "ami_id" { value = "${aws_instance.web.ami_id}" } info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

10. Components of output ● Multiple output variables can be configured with multiple output blocks. ● Parameter: ○ Value (string, list, map) ○ Description ○ Depends_on (list) # dependencies will be created before this output value is processed ○ Sensitive (boolean) # confidential, value is not revealed, can be seen by “terraform output” info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

11. modules ● Modules - ○ encapsulate groups of resources in your infrastructure. ○ are subdirectories with self-contained terraform code ○ may be sourced from Git, Mercurial, HTTPS locations ○ use variables and outputs to pass data ● All attributes within the block must correspond to variables within the module, with exception of : ○ Source ○ Version ○ provider info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

12. Terraform WorkFlow ● Write - Author infrastructure as code. ● Plan - Preview changes before applying. ● Apply - Provision reproducible infrastructure. info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

13. Configuration ● Terraform format ends in : ● .tf ● .tf.json ● Multiple file formats can be present in the same directory. info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

14. Lab: Writing first Terraform script info@zekeLabs.com | www.zekeLabs.com | +91 8095465880

15. Visit : www.zekeLabs.com for more details THANK YOU Let us know how can we help your organization to Upskill the employees to stay updated in the ever-evolving IT Industry. Get in touch: www.zekeLabs.com | +91-8095465880 | info@zekeLabs.com

02 terraform core concepts

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