VMware-Guide
VMware Guide
https://github.com/mikeroyal/VMware-Guide
Last synced: 18 days ago
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Bash/PowerShell Learning Resources
- Introduction to Bash Shell Scripting by Coursera
- Bash: Shell Script Basics by Pluralsight
- Bash/Shell by Codecademy
- Windows Remote Management in Ansible using PowerShell
- PowerShell in Azure Cloud Shell
- Azure Functions using PowerShell
- Azure Automation runbooks
- Using Visual Studio Code for PowerShell Development
- Integrated Terminal in Visual Studio Code
- AWS Tools for Windows PowerShell
- AWS Command Line Interface and aws-shell Sample for AWS Cloud9
- Configuring Cloud Shell on Google Cloud
- Google's Shell Style Guide
- PowerShell Best Practices and Style Guide
- Integrated Terminal in Visual Studio Code
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Bash/ PowerShell Tools
- Bash - compatible shell that integrates together useful features from the Korn shell (ksh) and the C shell (csh).
- PowerShell Core - platform (Windows, Linux, and macOS) automation and configuration tool/framework that works well with your existing tools and is optimized for dealing with structured data (JSON, CSV, XML, etc.), REST APIs, and object models. It also includes a command-line shell, an associated scripting language and a framework for processing cmdlets.
- AWS Shell - line shell program that provides convenience and productivity features to help both new and advanced users of the AWS Command Line Interface.
- VS Code Bash Debug
- VS Code Bash IDE - lsp/bash-language-server/blob/master/bash-lsp), that is based on [Tree Sitter](https://github.com/tree-sitter/tree-sitter) and its [grammar for Bash](https://github.com/tree-sitter/tree-sitter-bash) and supports [explainshell](https://explainshell.com/) integration.
- Windows Subsystem for Linux (WSL)
- Azure PowerShell
- Google Cloud Shell - based command-line access for managing your infrastructure and applications on Google Cloud Platform.
- Azure PowerShell
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Database Learning Resources
- SQL
- SQL Tutorial by W3Schools
- Learn SQL Skills Online from Coursera
- SQL Courses Online from Udemy
- SQL Online Training Courses from LinkedIn Learning
- Learn SQL For Free from Codecademy
- GitLab's SQL Style Guide
- OracleDB SQL Style Guide Basics
- Databases on AWS
- Best Practices and Recommendations for SQL Server Clustering in AWS EC2.
- Connecting from Google Kubernetes Engine to a Cloud SQL instance.
- MySQL Certifications
- What is NoSQL?
- Tableau CRM: BI Software and Tools
- SQL vs. NoSQL Databases: What's the Difference?
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Databases and Tools
- MSSQL for Visual Studio Code
- SQL Server Migration Assistant
- SQL Server Business Intelligence(BI)
- Tableau - releases/press-release-details/2019/Salesforce-Completes-Acquisition-of-Tableau/default.aspx).
- DataGrip - sensitive code completion, helping you to write SQL code faster. Completion is aware of the tables structure, foreign keys, and even database objects created in code you're editing.
- MySQL - native applications using the world's most popular open source database.
- PostgreSQL - relational database system with over 30 years of active development that has earned it a strong reputation for reliability, feature robustness, and performance.
- Amazon DynamoDB - value and document database that delivers single-digit millisecond performance at any scale. It is a fully managed, multiregion, multimaster, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications.
- FoundationDB - value store and employs ACID transactions for all operations. It is especially well-suited for read/write workloads but also has excellent performance for write-intensive workloads. FoundationDB was acquired by [Apple in 2015](https://techcrunch.com/2015/03/24/apple-acquires-durable-database-company-foundationdb/).
- CouchbaseDB - model NoSQL document-oriented database](https://en.wikipedia.org/wiki/Multi-model_database). It creates a key-value store with managed cache for sub-millisecond data operations, with purpose-built indexers for efficient queries and a powerful query engine for executing SQL queries.
- MongoDB - like documents.
- OracleDB - critical data with the highest availability, reliability, and security.
- MariaDB - critical applications.
- SQLite - language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine.SQLite is the most used database engine in the world. SQLite is built into all mobile phones and most computers and comes bundled inside countless other applications that people use every day.
- SQLite Database Browser
- dbWatch - premise, hybrid/cloud database environments.
- Cosmos DB Profiler - time visual debugger allowing a development team to gain valuable insight and perspective into their usage of Cosmos DB database. It identifies over a dozen suspicious behaviors from your application’s interaction with Cosmos DB.
- Toad - in expertise. This SQL management tool resolve issues, manage change and promote the highest levels of code quality for both relational and non-relational databases.
- Sequel Pro
- Azure Data Studio
- VMware
- Azure Synapse Analytics
- Azure SQL Managed Instance - premises applications to the cloud with very few application and database changes. Managed instance has split compute and storage components.
- Azure SQL Database - powered and automated features that optimize performance and durability for you. Serverless compute and Hyperscale storage options automatically scale resources on demand, so you can focus on building new applications without worrying about storage size or resource management.
- Adminer
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Go Learning Resources
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Go Tools
- golang tools
- Go in Visual Studio Code
- NATS - premise, in the cloud, at the edge, and even on a Raspberry Pi. NATS can secure and simplify design and operation of modern distributed systems.
- Fiber
- Traefik
- Gitea - hosted git service. Using Go, this can be done with an independent binary distribution across all platforms which Go supports, including Linux, macOS, and Windows on x86, amd64, ARM and PowerPC architectures.
- OpenFaaS - driven functions and microservices to Kubernetes without repetitive, boiler-plate coding. Package your code or an existing binary in a Docker image to get a highly scalable endpoint with auto-scaling and metrics.
- micro - based text editor that aims to be easy to use and intuitive, while also taking advantage of the capabilities of modern terminals. As its name indicates, micro aims to be somewhat of a successor to the nano editor by being easy to install and use. It strives to be enjoyable as a full-time editor for people who prefer to work in a terminal, or those who regularly edit files over SSH.
- Gravitational Teleport - over-HTTPS in a browser or Kubernetes clusters.
- Act
- Glide
- BadgerDB - value (KV) database written in pure Go. It is the underlying database for [Dgraph](https://dgraph.io/), a fast, distributed graph database. It's meant to be a performant alternative to non-Go-based key-value stores like RocksDB.
- Go kit
- Codis
- zap
- HttpRouter
- Gorilla WebSocket
- Delve
- GORM
- Go Patterns
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Java Learning Resources
- Java
- The Eclipse Foundation
- Getting Started with Java
- Oracle Java certifications from Oracle University
- Java Tutorial by W3Schools
- Getting Started with Java in Visual Studio Code
- Google Java Style Guide
- Chromium Java style guide
- Get Started with OR-Tools for Java
- Gradle User Manual
- Google Developers Training
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Kubernetes Learning Resources
- Getting started with Kubernetes on AWS
- Getting started with Google Cloud
- Getting started with Kubernetes on Red Hat
- YAML basics in Kubernetes
- Elastic Cloud on Kubernetes
- Simplify Machine Learning Inference on Kubernetes with Amazon SageMaker Operators
- Kubernetes Across VMware vRealize Automation
- VMware Tanzu Kubernetes Grid
- All the Ways VMware Tanzu Works with AWS
- VMware Tanzu Education
- Using Ansible in a Cloud-Native Kubernetes Environment
- Setting up a Kubernetes cluster using Vagrant and Ansible
- Kubernetes Fluentd
- Understanding the new GitLab Kubernetes Agent
- Kubernetes Contributors
- KubeAcademy from VMware
- Docker and Kubernetes
- Kubernetes on Microsoft Azure
- YAML basics in Kubernetes
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ML frameworks & applications
- PyTorch
- Amazon SageMaker
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Apache MXNet
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- Anaconda
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Apache PredictionIO
- BigDL
- Apache Spark™ MLflow
- MLflow Tracking
- MLflow Projects
- MLflow Models
- Model Registry
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
- Tensorman
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- PlaidML
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- nGraph - of-use to AI developers.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Cluster Manager for Apache Kafka(CMAK)
- Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
- cuML - learn.
- AutoGluon - accuracy deep learning models on tabular, image, and text data.
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
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ML Learning Resources
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.NET Learning Resources
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Networking Learning Resources
- AWS Certified Security - Specialty Certification
- Cisco Security Certifications
- The Red Hat Certified Specialist in Security: Linux
- Linux Professional Institute LPIC-3 Enterprise Security Certification
- Networking courses and specializations from Coursera
- Network & Security Courses from Udemy
- Network & Security Courses from edX
- Google Cloud Certified Professional Cloud Security Engineer
- Cisco Security Certifications
- Cybersecurity Courses and Certifications by Offensive Security
- Linux Professional Institute LPIC-3 Enterprise Security Certification
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Node.js Learning Resources
- The OpenJS Foundation
- Getting started with Node.js in Google Cloud
- Getting Started with Node.js in AWS
- Introduction to Node.js by W3Schools
- Node.js tutorial in Visual Studio Code
- Node.js Build Working Group
- Node.js App Hosting & Deployment in Microsoft Azure
- The Node.js best practices list
- The Node.js Community Committee
- Node.js Mentorship Program Initiative
- Node.js - side scripts outside of a browser.
- Server-side Development with NodeJS, Express and MongoDB on Coursera
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Node.js Tools
- NestJS
- Meteor - simple environment for building modern web applications with JavavScript.
- NPM
- nvm - user, and invoked per-shell. nvm works on any POSIX-compliant shell (sh, dash, ksh, zsh, bash), in particular on these platforms: unix, macOS, and windows WSL.
- node-docker
- Express
- NW.js
- PM2 - in load balancer. It allows you to keep applications alive forever, to reload them without downtime and to facilitate common system admin tasks.
- jenkins-nodejs - installer, allowing to create as many NodeJS installations "profiles" as you want.
- Strapi
- Standard
- Hexo
- node-gyp - platform command-line tool written in Node.js for compiling native addon modules for Node.js. It contains a vendored copy of the gyp-next project that was previously used by the Chromium team, extended to support the development of Node.js native addons.
- Mocha
- AVA
Programming Languages
Categories
Tools
79
Open Source Security Learning Resources
43
Security Tools
34
ML frameworks & applications
31
Python Frameworks and Tools
28
Databases and Tools
25
Go Tools
20
Kubernetes Learning Resources
19
Telco Learning Resources
19
Node.js Tools
18
Bash/PowerShell Learning Resources
15
Database Learning Resources
15
Python Learning Resources
12
Node.js Learning Resources
12
Java Learning Resources
11
Networking Learning Resources
11
Virtualization
10
Security Standards, Frameworks and Benchmarks
9
Go Learning Resources
9
Bash/ PowerShell Tools
9
TypeScript Learning Resources
9
ML Learning Resources
4
.NET Learning Resources
3
License
1
Protocols
1
Sub Categories
Keywords
golang
15
python
14
go
13
nodejs
13
javascript
9
typescript
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java
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node
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dotnet
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security
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kubernetes
5
testing
4
deep-learning
4
android
4
machine-learning
4
http
4
performance
3
electron
3
rest
3
c-sharp
3
cli
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devops
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neural-network
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github-actions
2
web
2
language-server-protocol
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json
2
serverless
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scheduling
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tracing
2
design-patterns
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fuzz-testing
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fuzzing
2
data-science
2
microservices
2
k8s
2
lambda
2
mocha
2
kernel
2
docker
2
terminal
2
editor
2
microsoft
2
types
2
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2
tensorflow
2
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kotlin
2