Establish The Core of Cloud Computing Application by Using Hazelcast (Chinese)Joseph Kuo
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The concept of cloud computing has been introduced for several years. Many of us may be able to roughly imagine what it is, some of us may know how to describe it, but only a few do know how to implement it. Does NoSQL, MapReduce or Big Data equal to cloud computing? Can a service be said that it is cloud-based if it is using any of those tools? Many companies and groups have declared that their online services are cloud-based or they are using cloud computing, but are those all true? Except for the questions above, where should we start if we would like to establish a cloud-based service which is distributed, flexible, reliable, available, scalable and stable? This session intends to lead you through the gate of mysteries and head to the beautiful realm of cloud computing by using powerful tools, like Hazelcast. Welcome to journey with us to the core of cloud computing application!
https://cyberjos.blog/java/seminar/jcconf-2014-establish-the-core-of-cloud-computing-application-by-using-hazelcast/
簡化 JVM 上雲 - 透過 Azure Spring Cloud 提升開發、發佈及服務監控效率Shengyou Fan
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Spring Boot 一直是 Java 開發生態系裡市佔率最高的框架,許多企業都採用其開發自身服務。隨著開發典範的轉移,即便 Spring 提供完整方案,開發者往往對架構修改及服務管理的工作怯步,是否移轉上雲也有所疑慮。在這場分享裡,將會介紹由 Azure 提供的 Spring Cloud 解決方案,並從最簡單的一個 Spring Boot 應用開始,逐步導入微服務架構、連接 Azure DB、藍綠部署到服務監控,讓開發者了解使用 Azure 運行 Spring 是一個簡單又有效率的體驗,加速將 JVM 應用上雲。
Kubernetes Basics provides an overview of Kubernetes concepts and components. It discusses pods vs deployments, scaling deployments, rolling updates, stateful vs stateless applications, daemon sets, secrets, configmaps, services, ingress, storage classes, network policies, and Kubernetes CLI commands. Hands-on examples are given for running commands, exposing services, deleting resources, executing commands in pods, viewing logs, and getting resource information. YAML files are shown for defining deployments, services, and ingress. Skills discussed include using configmaps as environment variables, sidecar deployments, init containers, labels and node selectors, private registries, taints and tolerations, resource management, and readiness and liveness probes.
Simon Su presented on using Google Cloud Platform for IoT applications. The document discussed key concepts in IoT like connectivity, devices, sensors and cloud infrastructure. It provided examples of using various Google Cloud services for IoT like Cloud PubSub for messaging, BigQuery for data storage, Cloud Functions for serverless computing, Cloud Vision API for image recognition and Cloud IoT Core for connecting devices. Code samples were given to illustrate how to use these services for common IoT tasks.
This document provides an overview of various Google Cloud Platform services including Compute Engine, Networking, Load Balancing, Cloud Launcher, Cloud Storage, Cloud SQL, Cloud Monitoring, Cloud DNS, and Deployment Manager. It includes descriptions of the basic concepts and functionality for each service. It also outlines several hands-on labs demonstrating how to use specific GCP services like backing up instances to Cloud Storage snapshots, exporting Cloud SQL databases to Cloud Storage, enabling Cloud Logging, and deploying a VM instance using Deployment Manager.
This document provides instructions for enabling and using the serial console on a Windows machine. It outlines connecting to the VM from port 3, using "?" to check available commands, running "cmd" to start a new command session, and "ch -sn [session name]" to switch sessions. Finally, it notes you can reset the password using "Create or reset Windows password" and login to access the Windows command prompt for troubleshooting.
Cloud Spanner is a fully managed relational database that provides global scale, SQL support, and strong consistency across multiple regions. It is horizontally scalable, provides automatic replication and maintenance, and supports transactions with ACID semantics. Spanner offers high availability, enterprise-grade security with encryption and IAM controls, and supports multiple programming languages through client libraries. Performance scales based on the number of nodes provisioned, with a minimum of 3 nodes recommended for production workloads.
Google Cloud Computing compares GCE, GAE and GKESimon Su
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Google Cloud Computing compares GCE, GKE and GAE. GCE provides raw compute, storage and networking resources and requires more management overhead. GAE focuses on application logic and requires less management. GKE offers managed Kubernetes infrastructure and services. Each option has different strengths for workloads like microservices, containerized services, or large-scale applications requiring quick scaling. Monitoring and management features like Stackdriver are also compared.
The document provides information about Simon Su and his expertise in Google Dataflow. It includes Simon's contact information and links to his online profiles. It then discusses Simon's areas of specialization including data scientist, data engineer, and frontend engineer. The document proceeds to provide information about preparing for a Google Dataflow workshop, including documents and labs to review. It also discusses Google Cloud services for data processing and analysis like Dataflow, BigQuery, Pub/Sub, and Dataproc. Finally, it outlines the agenda for the workshop, which will include hands-on labs to deploy users' first Dataflow project and create a streaming Dataflow model.
This document outlines labs for Google Cloud Dataflow workshops. Lab 1 covers setting up the Dataflow environment and building a first project. Lab 2 focuses on deploying the first project to Google Cloud Platform. Lab 3 builds streaming Dataflow by creating PubSub topics/subscriptions and deploying streaming samples that read from PubSub and write to BigQuery.
GCPUG meetup 201610 - Dataflow IntroductionSimon Su
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This document provides information about Simon Su and Sunny Hu, who will be presenting on Google's BigData solution. It includes their contact information and backgrounds. Simon's areas of focus include Node.js and blogging. Sunny's skills include project management, system analysis, and Java. The document also advertises a Facebook and Google+ group for the Google Cloud Platform User Group Taiwan, where people can share experiences using GCP. It poses trivia questions about Google's infrastructure and provides timelines of Google's BigData innovations.
使用 Raspberry pi + fluentd + gcp cloud logging, big query 做iot 資料搜集與分析Simon Su
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This is a short training for introduce Pi to use fluentd to collect data and use Google Cloud Logging and BigQuery as backend and then use Apps Script and Google Sheet as presentation layer.
This document provides an overview of Docker concepts and commands for building, running, and managing Docker containers. It demonstrates how to run a simple Node.js application as a Docker container using commands like docker run, docker build, docker ps, and docker-compose. It also shows how to link containers, mount folders, push images to Docker Hub, and remove containers.
Google Cloud Platform Introduction - 2016Q3Simon Su
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The document summarizes news and services from Google Cloud Platform, including free GCE machine types, preemptible VMs, IAM project management, and new APIs for Machine Learning, Vision, and Speech. It also provides an overview of various GCP computing, storage, database and analytics services like Compute Engine, App Engine, Cloud SQL, Cloud Storage, BigQuery, and Dataflow. Join the Google Cloud Platform User Group Taiwan Facebook group for more information on GCP services and events.
Google Cloud Platform - for Mobile SolutionsSimon Su
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This document discusses Google Cloud Platform solutions for mobile development. It introduces several Google Cloud services useful for mobile backends including Google App Engine, Cloud Endpoints, Cloud Datastore, Google Cloud Messaging, Pub/Sub Messaging and Firebase. It provides overviews of how each service works and how they can help with building mobile apps and backends without having to manage complex infrastructure. The document aims to help mobile developers learn about Google Cloud options for building scalable and flexible cloud-based backends for their mobile applications.
JCConf 2015 - 輕鬆學google的雲端開發 - Google App Engine入門(下)Simon Su
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Google App Engine provides various developer tools and services to build cloud applications easily. These include Cloud Logging for viewing logs, Cloud Debugging for debugging applications, and Cloud Monitoring for integrating with monitoring systems. It also provides security scanning. Developers can use modules and managed virtual machines on App Engine to build applications. Common runtimes on managed VMs include Node.js, Python, Java, and Go. Local testing and deployment to the cloud is simplified.
2. We are from
GCPUG.TW~
大家好,我們是Google Cloud Platform
User Group (GCPUG) 台灣分支,我們是一
個Google Cloud Platform 相關技術的民間
社群,成立的宗旨在分享與交換Google
Cloud Platform 上的一些技術與使用經驗。
歡迎對 Google Cloud Platform 有興趣的朋
友們可以共襄盛舉。
3. Outline
● Google Container Engine (GKE) Introduction
● Cloud Source Repositories
● Container Registry service
● Container Builder with CICD