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Oracle-Cloud-Guide

Oracle Cloud Infrastructure Guide
https://github.com/mikeroyal/Oracle-Cloud-Guide

Last synced: 13 days ago
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Database Learning Resources

Databases and Tools

  • 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.
  • 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.
  • 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.
  • 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
  • VMware
  • Adminer

Go Learning Resources

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
  • Oracle Cloud SDK for Python
  • 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, 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

Java Learning Resources

Kubernetes Learning Resources

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. 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, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
  • 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 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.
  • Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.

ML Learning Resources

.NET Learning Resources

Networking Learning Resources

Node.js Learning Resources

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
  • Oracle Cloud SDK for Java
  • 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
  • egg
  • mysqljs
  • axios

Open Source Security Learning Resources