DEV Community: tarak-brainboard

📌 How to generate cloud infrastructures with AI?

tarak-brainboard — Sat, 03 Feb 2024 17:00:00 +0000

Generating cloud infrastructures with AI involves automating the process of provisioning and managing cloud resources using artificial intelligence techniques. Here's a medium-level overview of the steps involved in generating cloud infrastructures with AI:

I. Define Your Requirements

Start by defining your infrastructure requirements. Determine what type of applications or services you want to deploy, the scale of your infrastructure, and any specific performance, security, or compliance requirements.

The prompt I used in the example: "Suggest a serverless architecture on AWS for a mobile backend, including API Gateway, Lambda functions, and database integration."

II. Choose Your Cloud Provider

Select a cloud provider that aligns with your needs (e.g., AWS, Azure, Google Cloud, etc.). Different providers offer various services and features, so choose one that best suits your project.

III.Infrastructure as Code (IaC)

Use IaC to define your cloud infrastructure in code. IaC allows you to create, update, and manage cloud resources in a reproducible and automated way.

Do not forget to configure your resources according to your specs. If not your Terraform code might not be working.

IV. Testing and Validation

Before deploying your AI-driven infrastructure into production, thoroughly test and validate it to ensure that it meets your requirements and performs as expected. Consider using Terraform Validate and Terraform Plan.

V. Continuous monitoring

Implement continuous monitoring of your infrastructure to help you detect anomalies, security threats, and understand the cost of your infrastructure in real-time. Tools like Tfsec, Terrascan, and Infrascost can be used for monitoring.

By following these steps, you can leverage AI to automate the generation, management, and optimization of your cloud infrastructure, making it more efficient, cost-effective, and responsive to changing demands.

VI. Documentation

Maintain comprehensive documentation for your AI infrastructure and provide training to your team to effectively manage and operate the infrastructure.

VII. Example of Prompts

When using AI to generate cloud infrastructure ideas or configurations, you can provide prompts that specify your requirements, preferences, or the context of the infrastructure you need. Here are some examples of prompts that you might use:

Basic Cloud Setup: "Design a basic cloud infrastructure for a small startup using AWS. Include a web server, database, and basic security measures."
Scalable E-commerce Platform: "Create a scalable cloud infrastructure plan for an e-commerce website on Azure, focusing on high availability and load balancing."
Hybrid Cloud Configuration: "Suggest a hybrid cloud infrastructure that integrates on-premises data centers with Google Cloud services for a financial institution."
Cost-Effective Solution for Non-Profit: "Propose a cost-effective cloud infrastructure on AWS for a non-profit organization, ensuring low maintenance and ease of use."
High-Performance Computing (HPC): "Outline a cloud infrastructure on AWS for high-performance computing tasks, including compute-optimized instances and storage solutions."
Disaster Recovery Plan: "Develop a disaster recovery plan for a cloud infrastructure on Microsoft Azure, ensuring data redundancy and minimal downtime."
IoT Device Management: "Design a cloud infrastructure on Google Cloud Platform for managing IoT devices, with a focus on data processing, storage, and security."
AI and Machine Learning Workloads: "Create a cloud infrastructure layout on AWS for AI and machine learning workloads, including necessary compute and GPU resources."
Multi-Region Deployment: "Plan a multi-region deployment on Azure for a global application, focusing on latency reduction and regional compliance."
Serverless Architecture: "Suggest a serverless architecture on AWS for a mobile backend, including API Gateway, Lambda functions, and database integration."
Secure Financial Services Infrastructure: "Propose a secure and compliant cloud infrastructure on AWS for a fintech company, with emphasis on encryption and regulatory compliance."
Media Streaming Service: "Outline a cloud infrastructure for a high-traffic media streaming service on Azure, ensuring efficient content delivery and caching."
Big Data Analytics: "Develop a cloud infrastructure plan on AWS for big data analytics, including data lakes, ETL processes, and analytics tools."
Mobile App Hosting: "Suggest a cloud hosting solution on Google Cloud for a rapidly growing mobile application, focusing on scalability and database performance."

Remember, the more specific your prompt is regarding your needs and constraints, the more tailored and useful the response will be.

Request your access now and start building your cloud infrastructures with AI on Brainboard here.

📌 AWS 3 tier architecture with LBs, ASG and RDS

tarak-brainboard — Thu, 01 Feb 2024 17:00:00 +0000

❶ Description 📝

This infrastructure is designed for fault tolerance and high availability, using multiple availability zones and auto-scaling features.

❷ Architecture components 🏛️

VPC and Networking

aws_vpc: The foundational networking component that provides a private, isolated section of the AWS cloud where you can launch AWS resources in a virtual network that you define.
aws_subnet: Subdivides your VPC into smaller networks. Each subnet is in a specific availability zone and is used to isolate and control traffic flow for the web, app, and database tiers.
aws_internet_gateway: Connects the VPC to the internet, allowing communication between instances in your VPC and the internet.
aws_nat_gateway: Used to enable instances in a private subnet to connect to the internet or other AWS services while preventing incoming internet connections.

Auto Scaling and Load Balancing

aws_autoscaling_group: Manages a group of EC2 instances, automatically adjusting the number of instances to maintain consistent performance and handle loads efficiently.
aws_elb (Elastic Load Balancer): Automatically distributes incoming application traffic across multiple targets, such as EC2 instances, ensuring high availability and fault tolerance.

DNS and WAF

aws_route53_zone and aws_route53_record: Route 53 is Amazon’s DNS service, managing domain names and translating friendly domains into IP addresses (A and CNAME records).
aws_waf_web_acl, aws_waf_rule, aws_waf_ipset: AWS WAF protects your web applications from common web exploits and bots that could affect application availability, compromise security, or consume excessive resources.

Database and Storage

aws_db_subnet_group: Defines subnets for your database cluster in RDS to operate across different availability zones for fault tolerance.
aws_rds_cluster: An Amazon Aurora PostgreSQL-compatible edition cluster provides enhanced performance and scalability for database operations.
aws_s3_bucket and aws_s3_bucket_versioning: Defines an S3 bucket for object storage, with versioning enabled to preserve, retrieve, and restore every version of every object stored in your Amazon S3 bucket.

Elastic IPs and Launch Templates

aws_eip (Elastic IP): Provides a static IPv4 address for dynamic cloud computing, allowing you to manage the public IP of your instances.
aws_launch_template: Provides a template for launching EC2 instances, ensuring that every instance launched has the same configuration.

🛡️ Once you clone this architecture from the template catalog, you can also scan it with the native CI/CD engine to know its security posture, its cost, before you deploy it.

😍Also, when you communicate with your colleagues, it’s much easier to show the architecture and explain. Not everyone is a Terraform guru.

🚀 You can get it here: https://app.brainboard.co.

AWS Services Mindmap

tarak-brainboard — Tue, 30 Jan 2024 17:00:00 +0000

Networking:

The Networking category in AWS offers a range of services to manage and optimize network resources. These services enable you to build, secure, and manage network infrastructure, ensuring efficient data flow and connectivity between different components of your applications.

AWS Transit Gateway: Central hub for connecting VPCs and on-premises networks.
AWS Global Accelerator: Optimizes application performance globally.
AWS VPN CloudHub: Secure hub-and-spoke model for site-to-site connectivity.
Amazon CloudFront: High-speed global content delivery network.
AWS Elastic Load Balancing (ELB): Balances incoming traffic across multiple targets.
AWS PrivateLink: Private connectivity to AWS services, minimizing public internet exposure.
Amazon Route 53: Scalable DNS web service for routing end-users to applications.
AWS Direct Connect: Private network connection between on-premises and AWS.
AWS App Mesh: Manages microservices communication.

Compute:

The Compute category provides services that allow you to run and manage applications and workloads. It offers various options for compute resources, from virtual servers to serverless computing, enabling you to scale your applications easily while optimizing costs and performance.

Amazon EC2 Auto Scaling: Automatically adjusts EC2 capacity for consistent performance.
AWS Serverless Application Repository: Marketplace for serverless applications.
Amazon WorkSpaces: Secure, managed Desktop-as-a-Service (DaaS).
AWS Elastic Beanstalk: Easy-to-use service for deploying and scaling web applications.
Amazon EC2 Spot Instances: Access to spare compute capacity at reduced prices.
AWS Batch: Manages batch computing across EC2 instances.
Amazon EC2 (Elastic Compute Cloud): Scalable cloud computing capacity.
AWS Lambda: Runs code without server management.
Amazon Lightsail: Simplifies launching and managing virtual private servers.

Containers:

AWS Containers services are designed for managing and orchestrating containerized applications. Whether you need to run containers in the cloud or on-premises, AWS offers solutions to simplify container deployment and scaling, making it easier to manage container workloads.

Amazon ECS on AWS Outposts: Runs ECS in on-premises environments.
AWS App Runner: Simplifies containerized application development and deployment.
Amazon ECS (Elastic Container Service): Scalable container management service.
Amazon EKS (Elastic Kubernetes Service): Managed Kubernetes service.
AWS Fargate: Serverless compute engine for containers.
Amazon ECR (Elastic Container Registry): Managed Docker container registry.

Storage:

The Storage category includes services for storing, managing, and retrieving data. These services offer scalable and durable storage solutions, both in the cloud and for on-premises applications, ensuring data availability and reliability.

AWS Storage Gateway: Hybrid cloud storage service integrating on-premises environments with AWS.
Amazon EFS (Elastic File System): Scalable file storage for AWS and on-premises resources.
Amazon FSx for Lustre: High-performance file system optimized for fast data processing.
Amazon FSx for Windows File Server: Fully managed Windows native file system.
Amazon S3 (Simple Storage Service): Scalable object storage with high data availability and security.
Amazon EBS (Elastic Block Store): Block-level storage volumes for EC2 instances.
Amazon S3 Glacier: Low-cost storage service for data archiving and backup.

Database

AWS Database services provide scalable and managed database solutions, catering to various database models, including relational, NoSQL, and ledger databases. These services simplify database management, enhance performance, and ensure data durability.

Amazon DocumentDB (with MongoDB compatibility): Fast, scalable NoSQL database service.
Amazon Neptune: Fully managed graph database service.
Amazon Timestream: Time-series database for IoT and operational applications.
Amazon QLDB (Quantum Ledger Database): Immutable and verifiable ledger database.
Amazon Keyspaces (for Apache Cassandra): Managed Apache Cassandra-compatible database service.
Amazon RDS (Relational Database Service): Simplifies relational database setup, operation, and scaling.
Amazon DynamoDB: Key-value and document database with single-digit millisecond performance.
Amazon Aurora: High performance MySQL and PostgreSQL-compatible database.

Analytics:

The Analytics category offers services for processing, analyzing, and visualizing data. Whether you're dealing with big data or real-time analytics, AWS provides tools to help you discover insights, make data-driven decisions, and build intelligent applications.

AWS Glue: Serverless data integration service for ETL operations.
Amazon QuickSight: Business intelligence service for data visualization.
AWS Data Lake Formation: Streamlines setting up secure data lakes.
Amazon Kinesis Data Firehose: Real-time data streaming and loading service.
Amazon Redshift: Fast and scalable data warehouse service.
Amazon EMR (Elastic MapReduce): Big data platform for processing vast amounts of data.

Blockchain:

AWS Blockchain services enable you to create, deploy, and manage scalable blockchain networks. These services leverage open-source frameworks to streamline the development and operation of blockchain applications.

Amazon QLDB (Quantum Ledger Database): Central authority ledger database.
Amazon Managed Blockchain: Creates and manages blockchain networks.

IoT (Internet of Things)

AWS IoT services empower you to connect and manage IoT devices securely. They enable IoT data processing, analytics, and device management, allowing you to build IoT applications that harness the power of connected devices.

AWS IoT Device Defender: Audits and monitors IoT device configurations.
AWS IoT 1-Click: One-click creation of simple IoT device applications.
Amazon FreeRTOS: Operating system for microcontrollers in IoT devices.
AWS IoT Greengrass: Extends AWS to edge devices for local data processing.
AWS IoT Core: Securely connects IoT devices to the cloud.
AWS IoT Analytics: Analytics service for IoT device data.

Multimedia:

The Multimedia category encompasses services for handling video and media content. Whether you need to stream, process, or transcode media files, AWS offers solutions to deliver high-quality multimedia experiences.

Amazon Kinesis Video Streams: Streams video securely for analytics and ML.
Amazon IVS (Interactive Video Service): Adds live video to applications.
AWS Elemental MediaPackage: Video processing and delivery service.
AWS Elemental MediaLive: Live video processing service.
Amazon Elastic Transcoder: Cloud-based media transcoding service.

Security

AWS Security services focus on protecting your applications and data. These services include tools for identity and access management, DDoS protection, encryption, and compliance monitoring to ensure your infrastructure remains secure.

AWS Certificate Manager: Manages SSL/TLS certificates.
AWS Key Management Service (KMS): Manages cryptographic keys.
AWS Shield: DDoS protection service.
AWS WAF (Web Application Firewall): Protects against web exploits.
AWS IAM (Identity and Access Management): Manages access to AWS services and resources.
Amazon Inspector: Automated security assessment for applications.

Frontend & App Integration:

Frontend and App Integration services in AWS help you build and connect applications. They offer tools for API management, event-driven architectures, and real-time data synchronization, facilitating the creation of responsive and interactive applications.

AWS AppSync: GraphQL API for real-time data synchronization.
AWS EventBridge: Serverless event bus for application data integration.
AWS Step Functions: Orchestrates AWS services into serverless workflows.
Amazon SNS (Simple Notification Service): Messaging service for A2A and A2P communication.
Amazon API Gateway: API management and processing service.
AWS Amplify: Development platform for building web and mobile applications.

Machine Learning:

The Machine Learning category provides services for building, training, and deploying machine learning models. These services leverage artificial intelligence and deep learning technologies to extract insights, automate tasks, and enhance applications with AI capabilities.

Amazon Lex: Builds voice and text chatbots using deep learning.
Amazon Forecast: Time-series forecasting using machine learning.
Amazon SageMaker: Prepares, builds, trains, and deploys ML models.
Amazon Rekognition: Image and video analysis service.
Amazon Comprehend: NLP service for text analysis.
Amazon Polly: Converts text into lifelike speech.

Contact Center:

AWS Contact Center services enhance customer engagement and support. Whether you're looking to provide real-time information to agents or create applications without coding, AWS offers solutions to improve customer interactions.

Amazon Connect Wisdom: Provides real-time information for contact center agents.
Amazon Honeycode: Builds mobile and web applications without coding.
Amazon Pinpoint: Customer engagement service through various channels.
Amazon Connect: Cloud-based contact center service for customer service.

Build your AWS infrastructures here 👉 https://app.brainboard.co

📌 What's new in Brainboard?

tarak-brainboard — Sat, 20 Jan 2024 17:00:00 +0000

🚀 Contextual Smart Autosuggestions

Based on AI, the smart suggestion understands the context of the parameters of the resources and suggests an accurate list of values. It’s also organized into categories for efficient retrieval & selection. This allows you to build faster and avoid back and forth to the documentation.

🎨 Enhanced Node UI - First Look

We've reviewed how you design your cloud infrastructures in Brainboard and revamp our UX:

Advanced Node Options Bar: Effortlessly access styling features like background color through our redesigned options bar. Located conveniently near your selected node(s), it's now just a click away for seamless customization.
Versatile Contextual Menu: Right-click to discover a world of possibilities! Our new contextual menu houses all essential actions, from 'open cloud configuration' & 'hiding from code' to 'transforming into data', ensuring every action you need is right at your fingertips.

Stay tuned for our upcoming second iteration, promising even greater ease and flexibility in your Brainboard experience.

🎁 Recent Product Improvements

Revamped UI/UX for the ID Card: Experience a fresh, user-friendly interface with the ID card, now featuring a 'stick-to-top' header for easier navigation as you scroll.
Resource Rotation on Design Canvas: Introducing the ability to rotate resources on the canvas, adding a new dimension to your design capabilities.
Connector Enhancements: We've implemented 20 significant fixes to our Connectors, ensuring a smoother, more reliable experience.

Start designing your cloud infrastructures here 👉 https://app.brainboard.co

📌 How to Use the Azure DNS Resolver for Private Network DNS Management?

tarak-brainboard — Thu, 18 Jan 2024 17:00:00 +0000

❶ Description

Azure DNS Private Resolver enables you to query Azure DNS private zones from an on-premises environment and vice versa without deploying VM based DNS servers. This is a first-party service that eliminates the need for the IaaS system to be the DNS forwarder for the private endpoint name resolution.

❷ Architecture components

RGS: Containers for organizing related Azure services. We defined 2 RGS, one for the hub and one for the spoke.
VNets are used to create isolated networks within Azure. The configuration defines a hub network and a spoke network.
Subnets are subdivisions of a VNet. There are several subnets defined, including those for outbound DNS resolution, inbound DNS resolution, the default subnet for the hub, the subnet for Azure Firewall, and the default subnet for the spoke.
DNS Resolver provides DNS resolution within the VNet. There are resources defined for inbound and outbound endpoints, as well as forwarding rules.
Azure Firewall and Firewall Policy provide network security and filtering for the resources within the VNet.
Private DNS Resolver DNS Forwarding Ruleset and Forwarding Rule define how DNS queries are forwarded by the Azure DNS Resolver.
VMs are computing resources on which you can run applications and services. The configuration defines virtual machines for both the hub and the spoke.
PostgreSQL Server is a managed DB service that provides a PostgreSQL server in the spoke resource group.
Private Endpoint allows a private connection from the virtual network to Azure services, such as the PostgreSQL server.
VNet Peering establishes connectivity between the hub and spoke virtual networks.
Network Interfaces are attached to VMs or other resources to enable network connectivity.

🛡️ Once you clone this architecture from the template catalog, you can also scan it with the native CI/CD engine of Brainboard.co to know its security posture, its cost, before you deploy it.

😍 Also, when you communicate with your colleagues, it's much easier to show the architecture and explain. Not everyone is a Terraform guru.

🚀 You can get it here: https://app.brainboard.co.

📌 How to design and implement core networking infrastructure on Azure?

tarak-brainboard — Tue, 16 Jan 2024 17:00:00 +0000

Navigating the complexities of Azure's networking infrastructure requires a strategic approach, encompassing the technical aspects of configuration and considerations for security, cost management, scalability, and performance.

❶ IP Addressing and Network Segmentation in Azure

Effective IP management is the cornerstone of Azure's networking strategy, ensuring secure and efficient operations.
◆ Network Segmentation: Tailoring IP address spaces for different segments like production and development is crucial for enhancing security and traffic management. This involves defining clear boundaries and roles for each segment.
◆ VNets are instrumental in providing isolated network environments. Setting up VNets with specific address ranges is fundamental in creating secure, isolated operational spaces.
◆ Subnetting and Delegation: Optimizing network performance involves creating subnets for specific services (like gateways and firewalls) and delegating control within subnets for granular management.
◆ Public IP Management: Efficiently managing public IP addresses involves organizing them with prefixes and understanding when and how to use them, including the integration of external IP ranges for personalized addressing.

❷ Name Resolution and DNS Management

Robust DNS management within Azure is key to seamless domain name resolution and network integrity.
◆ Internal DNS Configuration: Setting up internal DNS within VNets ensures accurate domain name resolution, a fundamental aspect of network reliability.
◆ DNS Zone Management: Handling both public and private DNS zones is about balancing internet domain name resolution with internal network security and control.
◆ Azure DNS Private Resolver: Implementing this tool offers enhanced DNS resolution flexibility, catering to complex network setups.

❸ Connectivity and Routing in Azure

Efficient network connectivity and routing are pivotal for Azure's operational effectiveness.
◆ Service Chaining and VPNs: Planning network topologies for routing and access, including secure VPN connections, is essential for robust network architecture.
◆ VNet Peering and Network Management: Direct network access between VNets and centralized control over multiple VNets are crucial for streamlined operations.
◆ Custom Routing and Traffic Management: Implementing user-defined routes and managing traffic flow are key for tailored network performance.

❹ Network Monitoring

Maintaining network health and security is paramount in Azure's dynamic environment.
◆ Network Performance Insights: Tools like Azure Network Watcher provide essential insights into network performance and issues, enabling proactive management.
◆ Security Measures: Implementing DDoS protection and monitoring tools like Microsoft Defender for DNS are critical for safeguarding the network against emerging threats.

You can build your Azure infrastructures here 👉 https://app.brainboard.co.

📌 Azure Monitoring with Linux Web App

tarak-brainboard — Sat, 06 Jan 2024 08:00:00 +0000

I. Description 📝

This scenario addresses the monitoring services you can use and describes a dataflow model for use with multiple data sources. When it comes to monitoring, many tools and services work with Azure deployments. In this scenario, we choose readily available services precisely because they are easy to consume.

II. Architecture components 🏛️

Let's break down each resource:

azurerm_resource_group: This resource creates a Resource Group named "rg-main". Resource Groups in Azure are a fundamental entity used to group related resources for an application, making management, deployment, and monitoring easier.
azurerm_virtual_network: Defines a Virtual Network (VNet) named "vnet-kube". VNets enable Azure resources to securely communicate with each other, the internet, and on-premises networks.
azurerm_subnet: There are three subnet resources defined - subnet_webapp, subnet_db, and subnet_monitoring. Each subnet is a range of IP addresses in the VNet. They enable you to segment the network, improving security and performance. The subnets are named accordingly based on their intended use (for web apps, databases, and monitoring).
azurerm_public_ip: Creates a public IP address named "pip-kubernetes". Public IP addresses allow Azure resources to communicate with the internet and other Azure services.
azurerm_mariadb_server: This resource sets up a MariaDB server, which is a fully managed database service. The configuration includes version, storage size, administrator login details, and other settings.
azurerm_mariadb_database: Defines a MariaDB database named "mariadb_database" within the above MariaDB server. It specifies the character set and collation for the database.
azurerm_application_insights: Creates an Application Insights resource for monitoring the performance and usage of your apps. It's essential for diagnostics and telemetry.
azurerm_log_analytics_workspace: Sets up a Log Analytics workspace named "acctest-01". This workspace is used for managing and analyzing data logs collected by Azure services.
azurerm_linux_web_app: Creates a Linux-based web app service. It's part of the Azure App Service platform, which is used for hosting web applications.
azurerm_service_plan: Defines a service plan named "serviceplan", which specifies the hosting tier for the Azure web app. Service plans determine the location, features, cost, and compute resources associated with your web app.
azurerm_storage_account: Sets up a storage account named "storageaccountname". Azure Storage Accounts provide scalable cloud storage for data objects, file systems, messaging stores, and NoSQL stores.
azurerm_monitor_action_group: Creates an action group for Azure Monitor, which is used to define a set of actions to be executed when an alert is triggered.
azurerm_portal_dashboard: Establishes a custom dashboard in the Azure Portal named "my-dashboard". Dashboards are used for monitoring resources and data visualization.
azurerm_monitor_diagnostic_setting: Configures diagnostic settings for the MariaDB server. It specifies how metrics and logs are collected and stored, including integration with Log Analytics and a storage account.

III. Variables

In Terraform, a variable is a way to store and reuse values throughout your Terraform code. Variables are defined using the variable block and can be used to parameterize your Terraform code, making it more flexible and reusable.

IV. Readme

The readme file refers to a text file that provides information about the architecture, its features, requirements, installation instructions, and usage instructions.

The readme file will be displayed on the templates description when you publish your architecture.
The readme file will be pushed in git when you are using git as your repository.
The readme file will be cloned along with the design of your architecture.

V. How to use the architecture

To use this architecture, clone it within your project and change the following components:

Change the configuration of the cloud provider. In order to use the architecture you need to have a kubernetes cluster in place and change the resource group and name of the kubernetes cluster inside the configuration. Then change the variables:

VI. CI/CD

😍 You also have a complete CI/CD engine that allows you to check the security posture, estimate the cost of the infrastructure before deploying it and make sure that it respects your requirements.

👉 You can use the template here: https://app.brainboard.co

📌 How to design cloud architectures?

tarak-brainboard — Fri, 05 Jan 2024 08:00:00 +0000

I. Options bar

The options bar is a bar situated on top of the architecture that allows you to configure visual elements. It is always visible and can be moved around the page easily.

The options bar contains the following elements:

Change the grid to squares: This button allows you to change the grid to squares. It is useful when you want to align resources in the architecture.

Change the grid to dots: This button allows you to change the grid to dots.

Zoom in: This button allows you to zoom in on the architecture.

Fit content: This button allows you to automatically fit the content of the architecture in the screen. It is useful when you have zoomed in or out of the architecture and you want to go back to the original size.

Zoom out: This button allows you to zoom out the architecture.

Undo: This button allows you to undo the last action you did in the architecture.

Redo: This button allows you to redo the last action you did in the architecture.

Export the architecture in the Brainboard format: This button allows you to export the architecture in Brainboard format. It is useful when you want to save the architecture on your computer or share it with someone else.

Download the diagram: This button allows you to download the architecture in multiple formats: PNG, SVG or PDF. You can also include the background grid in the download or set a transparent background.

Create a new version: Brainboard allows you to create multiple versions of the same architecture. This button allows you to create a new version of the architecture. It is useful when you want to create a new version of the architecture before making changes to it.

Show versions: This button allows you to see the versions of the architecture. It is useful when you want to see the versions of the architecture and switch between them.

Readme: This button allows you to see the readme of the architecture. It is useful when want to create a proper description of the architecture so that other team members can better understand the use case of the architecture.

Shortcuts: This button allows you to see the shortcuts you can use inside the Brainboard editor. Knowing the shortcuts can help you speed up your workflow.

Templates button: The templates button is a button that allows you to open the templates modal. It is situated in the top right corner of the architecture.

Visibility action buttons: The visibility actions are a set of floating action buttons that allow you to show or hide elements (main grid, titles, connectors, depends_on connectors and icons) in the architecture. They are situated in the top right corner of the architecture.

Search resources: The search resources is a search bar that allows you to search for resources in the architecture. It is situated in the bottom center of the architecture and it allows you to search for resources by name or by type. If a resource matches your search, it will be highlighted in the architecture, while the rest of the resources that do not match will be greyed out.

Command palette: With just a quick keystroke (Command + K), the Command Palette opens up, allowing you to search and execute commands with lightning speed. Whether you're trying to troubleshoot an issue, switching from one architecture to another, or finding a specific configuration, you can easily find what you need by typing in your query. For example, if you want to "Create an architecture," simply type that phrase into the Command Palette and start performing complex tasks in no time.

You can watch how to use it on our YouTube channel: https://www.youtube.com/watch?v=0I-J9HlLYtI

😍 We're committed to continuously improving our product and welcome your feedback

You can design all your cloud architectures here 👉 https://app.brainboard.co

📌 Cloud Service Models: IaaS, PaaS, SaaS and Cloud Pricing Explained

tarak-brainboard — Thu, 04 Jan 2024 08:00:00 +0000

A profound understanding of diverse cloud service models and their pricing structures is crucial to effectively navigate, optimize, and budget for complex cloud-based solutions

As cloud architects, our role involves mastering complex systems, making strategic decisions, and optimizing cloud deployments, all while ensuring compliance and fostering effective collaboration with various stakeholders.

I. Infrastructure as a Service (IaaS)

This layer is foundational, allowing us to design and manage core infrastructure components. Key offerings include Azure VMs, AWS EC2 instances, and GCP Compute Engine. These services enable the provisioning and management of virtual machines, storage, and networking, offering the flexibility and scalability needed to tailor to our specific requirements.

II. Platform as a Service (PaaS)

Platforms like Azure App Service, AWS Elastic Beanstalk, and GCP App Engine significantly streamline the application development and deployment lifecycle. They offer pre-configured environments, automated scaling, and integrated services for databases, caching, and messaging, thus accelerating development processes.

III. Software as a Service (SaaS)

SaaS solutions minimize our involvement in managing software applications. Services such as Azure's Office 365, AWS's Amazon S3, and GCP's G Suite provide fully operational applications via the internet, eliminating the need for installation, patching, and infrastructure maintenance.

IV. Function as a Service (FaaS)/Serverless Computing

This model focuses on deploying discrete functions triggered by specific events. Azure Functions, AWS Lambda, and GCP Cloud Functions exemplify this, allowing us to write code for particular functionalities and incur costs only for the actual execution time of these functions.

V. Container as a Service (CaaS)

CaaS platforms address challenges in container management and orchestration, focusing on consistency, scalability, and portability. Azure AKS, AWS EKS, and GKE offer managed Kubernetes clusters, facilitating the deployment and management of containerized applications.

VI. Database as a Service (DBaaS)

DBaaS solutions manage infrastructure, backups, and scaling, freeing us to concentrate on data modeling and analysis. Azure SQL Database, AWS Amazon RDS, GCP Cloud Spanner, and Cloud SQL alleviate the burden of database server maintenance.

VII. Everything as a Service (XaaS)

Services like Azure Blob Storage, AWS Amazon GuardDuty, and GCP BigQuery represent XaaS offerings, enabling us to access advanced capabilities without investing in specialized infrastructure.

Cloud Pricing Models

I. Consumption-Based Pricing

Azure, AWS, and GCP offer this model, where costs are based on actual resource usage, such as compute instances, storage, and data transfers. This model is ideal for workloads with variable demands, allowing for dynamic resource scaling and cost optimization.

II. Pre-Paid Compute/Plans

This model offers cost predictability and potential savings. Azure Reserved Virtual Machine Instances, AWS Savings Plans, and GCP Committed Use Discounts provide discounted rates for upfront payments for a set amount of resources over a specific period. It's well-suited for stable workloads, offering financial flexibility and control.

You can find all these cloud services here 👉 https://app.brainboard.co/design

📌 Azure Spring Boot application architecture. Production grade.

tarak-brainboard — Wed, 03 Jan 2024 08:00:00 +0000

I. Description 📝

Azure Spring Apps is a platform as a service (PaaS) offering that allows developers to easily deploy and manage Spring Boot applications in the Azure cloud. To ensure high availability and fault tolerance, Azure provides the option to deploy Spring Apps in a zone-redundant configuration, which means the application is deployed across multiple availability zones within a region.

II. Architecture components 🏛️

Let's break down each Azure resource defined in this Terraform configuration:

- azurerm_resource_group: Creates a Resource Group named rg-main. Resource Groups are a fundamental element in Azure for grouping resources for management, billing, and access control.
- azurerm_virtual_network: Establishes a Virtual Network (vnet-main) with an address space 10.1.0.0/16. Virtual Networks are critical for creating a securely isolated environment for Azure resources.
- azurerm_key_vault: Sets up an Azure Key Vault (key_vault_main), used for securely storing and managing secrets, keys, and certificates. It includes access policies and settings for disk encryption.
- data azurerm_client_config: A data source that retrieves the configuration of the Azure provider. This is often used to fetch tenant and object IDs.
- random_string: Generates a random string (kv_random_string). This resource is typically used to create unique names or identifiers in Azure resources.
- subnet_keyvault: Creates a subnet within the vnet-main for the Key Vault with a specified address range.
- subnet_database: A subnet for database services, with delegation set for MySQL services.
- subnet_springapps: Another subnet designed for Spring applications.
- subnet_waf: Subnet presumably intended for a Web Application Firewall (WAF) with its own address range.
- azurerm_application_gateway: Configures an Application Gateway named application_gateway. This service provides application-level routing and is a load balancer for web traffic.
- azurerm_private_endpoint: Establishes a private endpoint (private_endpoint_keyvault) for the Key Vault, ensuring secure and private access within the Azure network.
- azurerm_mysql_database & azurerm_mysql_server: These resources provision an Azure MySQL database and its server, providing managed database services.
- azurerm_spring_cloud_app & azurerm_spring_cloud_service: Sets up a Spring Cloud application and service, facilitating the development of microservices-based applications.
- azurerm_private_dns_zone: Creates a private DNS zone (private_dns_zone), allowing for name resolution within a specific virtual network.
- azurerm_public_ip: Allocates a public IP address (public_ip), crucial for services that need to be accessible over the internet.
- azurerm_dns_zone: Defines a DNS zone (dns_zone) for domain name resolution. This resource is typically used for managing DNS records for domains.

Each of these resources is essential for different aspects of cloud architecture, ensuring scalability, security, and effective management of cloud services and applications on Azure.

III. Variables

IV. Readme

The readme file refers to a text file that provides information about the architecture, its features, requirements, installation instructions, and usage instructions.

The readme file will be displayed on the templates description when you publish your architecture.
The readme file will be pushed in git when you are using git as your repository.
The readme file will be cloned along with the design of your architecture.

V. How to use the architecture

To use this architecture, clone it within your project and change the following components:

Change the network addresses.

VI. CI/CD

😍 You also have a complete CI/CD engine in Brainboard.co that allows you to check the security posture and estimate the cost of the architecture before deploying it.

👉 It is available here: https://app.brainboard.co

📌 What is a Cloud architecture on Brainboard?

tarak-brainboard — Tue, 02 Jan 2024 08:00:00 +0000

Description

Cloud architecture represents a real infrastructure that is either deployed or to be deployed.

It is the container and the graphical face of your cloud resources.
Architectures are located within an environment inside a project.
In terms of Terraform, it has its own Terraform state file.
It supports modules and private git repository natively.
It has its own CI/CD pipelines to allow you to check its security posture, pricing, policies...before introducing any change.

Components

Think of the architecture as a git repository containing all cloud resources of your infrastructure.

1. Architecture information

All high-level information related to the architecture are accessible from the list of projects, environments and architectures.

Here is the explanation of all fields:

a. Name of the architecture.

b. Status of the architecture:

c. Description of the architecture.
d. Tags: list of tags.
e. Created at is a read-only field. The creation time stamp is in UTC.
f. Updated at is a read-only field. The last time this architecture has been updated in UTC format.
g. UUID: architecture unique identifier. This number is useful when you reach out to support.

2. Nodes

The node is a fundamental component of the architecture, it represents (in most cases) a cloud resource of a given cloud provider.

Every node has an identity card that contains its cloud configuration.
This identity card represents the Terraform configuration parameters of the node. Eg: azurerm_lb aws_instace google_app_engine_application

You can connect nodes by using the connectors from any selected node.

There are 5 types of nodes:

Cloud resource: this resource will be created when you provision the infrastructure.
Data source: it refers an existing cloud resource.
Module: Terraform module.
Container: it can contain other resource and give them its properties. For e.g. AWS VPC, or Azure virtual network.
Icon only: this resource is just graphic and has no Terraform representation.

3. Variables

These are Terraform variables and locals that you can define and use both on the design and during deployment.

4. Output

This is Terraform output that you can define and use both on the design and during deployment.

5. Readme file

You can write documentation for your infrastructure in markdown, and Brainboard generates the HTML version of it.

6. Versions

You can track every change you do to the infrastructure by creating as many versions as you want.

Design your cloud architectures here 👉 https://app.brainboard.co

📌 How to use Terraform - unsupported cloud providers?

tarak-brainboard — Fri, 22 Dec 2023 08:00:00 +0000

Description

Terraform supports a wide range of providers, including popular cloud providers, infrastructure providers, and SaaS providers.

They can be categorized as :

Official providers are owned and maintained by HashiCorp hashicorp
Partner providers are written, maintained, validated and published by third-party companies against their own APIs.
Community providers are published to the Terraform Registry by individual maintainers, groups of maintainers, or other members of the Terraform community.

Besides the providers that are supported by Brainboard (Azure, AWS, GCP, OCI, Scaleway), you can use all the other providers by following these steps:

Add the configuration for the cloud providers on the cloud provider configuration.
Add a new resource for that provider by using custom resources.

To illustrate it let's consider Palo Alto as a cloud provider that is not supported by Brainboard and see how we can use it.

Palo Alto Terraform Provider

The Palo Alto Networks Terraform provider is a plugin for Terraform that allows you to manage Palo Alto Networks resources, such as firewalls, in Terraform. The provider provides a set of Terraform resources that map to corresponding Palo Alto Networks resources, allowing you to manage your network infrastructure as code.

Cloud Provider Configuration

To configure the Palo Alto Networks provider you'll need to follow these steps:

Configure the provider Open the provider's configuration by clicking on the button next to the current cloud provider that you are using.

In the provider's block, you'll need to configure the Palo Alto Networks provider by specifying the required parameters. This may include the API key or other credentials needed to connect to the Palo Alto Networks platform. A sample provider configuration block could look like this:

Add a resource

Once the provider is configured, you can add a resource in Brainboard. For example, you could create a firewall rule in Palo Alto Networks like this:

At the bottom of the left menu, you can find the custom resources. This is a block where you can add terraform resources that are not supported yet by Brainboard.

Drag and drop the resource in the architecture schema that you need and add the needed information in the card id.

Type of the block: Add either resource if you want to provision a new resource or data if you want to use an existing one.
Type of the new resource: Add the type of the new resource from the cloud provider. In this example is paloalto_security_rule.
Resource name: Add a name for your new resource.
Terraform code of the resource Add the resource code and configuration into this field.
Icon: Add a custom icon by clicking on the icon at the beginning of the card id.

After finishing these steps you can use the resource as other supported resources in Brainbard and test it by running the terraform plan/apply command to create or update the resources in the Palo Alto Networks platform.

You can use them here 👉 https://app.brainboard.co