App Engine is Google's fully managed platform as a service that allows developers to build and run applications on Google's infrastructure. It provides several services including Cloud Datastore for scalable storage, Cloud SQL for relational databases, Cloud Storage for file storage, and Task Queues for background processing. Developers can build and deploy applications using App Engine's SDKs and APIs, and App Engine automatically scales applications up and down as traffic levels change.
Google Cloud Dataflow can be used to build TensorFlow pipelines. Dataflow allows training multiple TensorFlow models in parallel and writing results to Cloud Datastore. A sample pipeline shows generating training parameters, mapping over them to train models, and writing accuracy results to Cloud Storage. Dataflow provides autoscaling and machine types can be configured. The new DatastoreIO allows reading from and writing to Cloud Datastore from Dataflow pipelines using Protobuf and entity conversion helpers.
Google Cloud Platform 2014Q1 - Starter GuideSimon Su
This document provides an overview and introduction to Google Cloud Platform products and services including Cloud Datastore, Cloud Storage, Cloud SQL, BigQuery, App Engine, Compute Engine, and more. Key features and benefits are highlighted for each service such as scalability, availability, developer tools and SDKs, pricing models, and comparisons to other cloud offerings. Code samples and steps to get started with the services are also provided.
Google Cloud Computing compares GCE, GAE and GKESimon Su
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.
Creating autocomplete with elastic search on google cloudZareef Ahmed
This presentation give an architectural presentation on how elastic search can be used to create an auto suggest features using AngularJS and other tools on Google Cloud.
The document is a presentation about Google Compute Engine (GCE). It discusses cloud computing service levels including Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). GCE is described as an IaaS offering that provides virtual machines with flexible configurations and pricing based on minute usage. A demo is shown creating a VM and hosting a website using Apache. Another demo spins up a Hadoop cluster on GCE for distributed data processing.
Google Cloud Platform, Compute Engine, and App EngineCsaba Toth
Introduction to Google Cloud Platform's compute section, Google Compute Engine, Google App Engine. Place these technologies into the cloud service stack, and later show how Google blurs the boundaries of IaaS and PaaS.
This document summarizes a training course on Google Cloud Platform certification from Apponix Academy. The course introduces students to GCP infrastructure and services, teaching how to assess, install, and manage storage systems, networking, and application services. It aims to equip students with the knowledge and skills to become accredited GCP architects. Topics covered include cloud storage, identity and access management, and networking. Successful students can expect high salaries and strong career prospects due to the growing demand and market share of GCP. Basic Linux and command line skills are recommended prerequisites.
Google App Engine is a platform as a service that allows developers to build and host web applications at scale on Google's infrastructure. It handles all the complexities of scaling such as automatically increasing the number of application instances in response to traffic. Developers can write code in Python and other languages and App Engine provides APIs for common services like email, storage, databases and more so additional setup is not needed. It also automatically sends code to the nearest data center based on user location for fast performance.
Intro to the Google Cloud for DevelopersLynn Langit
This document provides an introduction to developing applications on Google Cloud. It discusses Google's cloud infrastructure and services like Compute Engine and App Engine. It demonstrates how to use the Google Cloud SDK and APIs to manage resources and build applications using various languages and tools. Specifically, it shows how to create instances in Compute Engine and Big Query, deploy applications to App Engine from Eclipse, and use command line tools to manage storage, databases and other services.
GAE can be adapted to work within China's Great Firewall through various techniques:
1. Custom domains can be used instead of *.appspot.com domains to avoid image and mail API restrictions.
2. Edge caching can be implemented for blobstore downloads to improve performance behind the Great Firewall.
3. Alternative services like local account authentication, custom mail servers, and domestic analytics tools can replace restricted Google services.
4. While some Google APIs may time out, others still function normally, so GAE is not impossible to use in China with some workarounds.
Powering your Apps via Google Cloud PlatformRomin Irani
Presentation at Google DevFest Ahmedabad, December 2014. This talk gives an overview of Google Cloud Platform and then goes into Cloud Endpoints and building out a simple IoT Project
A fresh look at Google’s Cloud by Mandy Waite Codemotion
Google, one of the early PaaS (Platform as a Service) pionneers, has recently substantially improved AppEngine, expanded its Cloud Platform to include CloudStorage, BigQuery and soon Google Compute Engine (still in early access as of this writing).
Google Cloud Platform Introduction - 2016Q3Simon Su
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.
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.
Google App Engine is a platform that allows developers to build and host Java web applications on Google's infrastructure. It handles tasks like scaling and maintenance automatically. Developers can focus on coding without worrying about managing hardware, servers, or other infrastructure. The platform provides APIs that allow access to Google services like Datastore, Memcache, and others. It also includes tools for deploying, monitoring, and managing applications.
Getting Started with Google's Infrastructure is summarized as follows:
1. Google Cloud Platform provides infrastructure services including virtual machines, networking, and storage hosted on Google's global network of data centers.
2. Google Compute Engine is an infrastructure as a service offering that allows users to launch and manage virtual machine instances.
3. The document provides an overview of Google Compute Engine including machine types, regions, persistent disks, load balancing, and pricing models.
Google Cloud Platform as a Backend Solution for your ProductSergey Smetanin
This document provides an overview of Google Cloud Platform services that could be used as a backend solution for a product. It discusses Google App Engine as a fully managed platform, Google Cloud Datastore as a NoSQL database, Google Cloud Storage for file storage, and Google BigQuery for analytics. The document then describes how a company called RuBeacon uses these Google Cloud services for their mobile app backend, focusing on App Engine, Datastore, Storage, and related services.
Cloud computing provides dynamically scalable resources as a service over the Internet. It addresses problems with traditional infrastructure like hard-to-scale systems that are costly and complex to manage. Cloud platforms like Google Cloud Platform provide computing services like Compute Engine VMs and App Engine PaaS, as well as storage, networking, databases and other services to build scalable applications without managing physical hardware. These services automatically scale as needed, reducing infrastructure costs and management complexity.
Developing Java Web Applications In Google App EngineTahir Akram
The document provides an overview of developing Java-based web applications using Google App Engine. It discusses the key features and services of GAE including the runtime environment, datastore, memcache, mail, task queues, images, cron jobs, and user authentication. It also covers limitations, demo examples, and resources for learning more.
This document provides an overview of Google App Engine, including what cloud computing is, the different types of cloud computing models, how App Engine provides a scalable infrastructure, the programming languages and frameworks supported, how data is stored and accessed via the datastore, services available on App Engine like caching, task queues, and mail, and tips for testing and deploying App Engine applications.
This document provides an overview of Google Cloud Platform (GCP) services. It discusses computing services like App Engine and Compute Engine for hosting applications. It covers storage options like Cloud Storage, Cloud Datastore and Cloud SQL. It also mentions big data services like BigQuery and machine learning services like Prediction API. The document provides brief descriptions of each service and highlights their key features. It includes code samples for using Prediction API to train a model and make predictions on new data.
This document provides an overview of Google App Engine for Java (GAE/J) through a presentation. It discusses key aspects of GAE/J including the scalable infrastructure, programming languages supported, frameworks, development tools, deployment, data storage using the datastore, testing, limits, and services. It emphasizes that GAE/J handles the infrastructure and allows developers to focus on application code without worrying about scaling or maintaining servers.
This document provides an overview of a workshop on building applications with Google App Engine. It introduces App Engine as an infrastructure and platform as a service, discusses key features like scalability. It also outlines the workshop agenda which includes setting up an App Engine project, building a basic URL shortener app using App Engine services like Datastore, URLFetch, Users API, Email and Memcache. Participants will have time to build their own URL shortening application.
Presentation copy of Google App Engine with hands-on presented at Cloud Computing Workshop at VTU,2014. Explored the fundamentals of Google App Engine and its features.
Also covers the instructions to set GAE locally and later to deploy on appengine.
Google App Engine is a PaaS that allows developers to build and host web applications in the Google cloud. The document summarizes a workshop on using the Java runtime environment on GAE. It discusses the SDKs, deploying and managing apps on GAE, data storage using the datastore, and limitations like the 30-second request limit. The biggest benefits of GAE are scalability and low startup costs, while the hardest limit is the 30-second request processing time.
This document provides an overview of using Google App Engine to develop a file repository application. It first discusses cloud computing and Google App Engine, including its architecture, key concepts like Bigtable distributed storage and the datastore. It then describes building a file repository app with functions like upload, download and file listing. The app is implemented using Java servlets, JSP, Apache Commons FileUpload and Google APIs.
Powerful Google Cloud tools for your hackwesley chun
This 1-hour presentation is meant to give univeresity hackathoners a deeper yes still high-level overview of Google Cloud and its developer APIs with the purpose of inspiring students to consider these products for their hacks. It follows and dives deeper into the products introduced at the opening ceremony lightning talk. Of particular focus are the serverless and machine learning platforms & APIs... tools that have an immediate impact on projects, alleviating the need to manage VMs, operating systems, etc., as well as dispensing with the need to have expertise with machine learning.
Scale with a smile with Google Cloud Platform At DevConTLV (June 2014)Ido Green
What is new and hot on Google Cloud?
How can you work like a pro with some (or all) the new APIs and services... Here are some good starting points to follow.
Introduction to Cloud Computing with Google Cloudwesley chun
This is a 20-30 minute technical talk introducing developers to cloud computing including an overview of Google Cloud computing products. There is a special focus on serverless tools as a convenient way for developers to run code. The talk ends with several inspirational apps showcasing what is possible with Google Cloud tools meant to plant a seed as to consider what is possible.
How Google Cloud Platform can help in the classroom/labwesley chun
This is a 90-min tech talk along with hands-on exercises gives a comprehensive, vendor-agnostic overview of cloud computing, primarily targeting educators in the higher education market but is open to any developer. This is followed by an introduction to products in Google Cloud Platform, focusing on its serverless and machine learning products. .
A guide to create a simple Java application and upload it to the Google Cloud Platform with Google App Engine. This presentation covers usage of persistence API with both Google Cloud SQL and Google Cloud Datastore.
Google App Engine is Google's Platform as a Service that allows users to run scalable web applications on Google's infrastructure. Written in Java or Python, apps run in a managed sandbox and use Google's non-relational Datastore and other services. Key features include automatic scaling, high availability, easy setup, and a free usage tier.
Java Web Programming on Google Cloud Platform [1/3] : Google App EngineIMC Institute
Google App Engine is a platform for hosting web applications in Google's data centers. It allows developers to build applications on scalable infrastructure without having to manage servers. Key features include automatic scaling, high availability, easy deployment, and built-in services like Datastore, Memcache and Task Queue. The development process involves using the App Engine SDK, which includes a local development server that emulates the live environment. Applications are deployed to App Engine by uploading the compiled code.
This slides are for developers who interest in Google Cloud Platform and how to deploy and run their application on Google infrastructures. It gives details on Cloud EndPoint and how build a unique backend for multiple frontend on multiple platform (Web, Android, iOS,..)
This document provides an introduction and overview of Google App Engine and developing applications with Python on the platform. It discusses what App Engine is, who uses it, how much it costs, recommended development tools and frameworks, and some of the key services provided like the datastore, blobstore, task queues, and URL fetch. It also notes some limitations of App Engine and alternatives to running your own version of the platform.
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 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.
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
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
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 - for Mobile SolutionsSimon Su
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
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.
Demystifying Neural Networks And Building Cybersecurity ApplicationsPriyanka Aash
In today's rapidly evolving technological landscape, Artificial Neural Networks (ANNs) have emerged as a cornerstone of artificial intelligence, revolutionizing various fields including cybersecurity. Inspired by the intricacies of the human brain, ANNs have a rich history and a complex structure that enables them to learn and make decisions. This blog aims to unravel the mysteries of neural networks, explore their mathematical foundations, and demonstrate their practical applications, particularly in building robust malware detection systems using Convolutional Neural Networks (CNNs).
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
Choosing the Best Outlook OST to PST Converter: Key Features and Considerationswebbyacad software
When looking for a good software utility to convert Outlook OST files to PST format, it is important to find one that is easy to use and has useful features. WebbyAcad OST to PST Converter Tool is a great choice because it is simple to use for anyone, whether you are tech-savvy or not. It can smoothly change your files to PST while keeping all your data safe and secure. Plus, it can handle large amounts of data and convert multiple files at once, which can save you a lot of time. It even comes with 24*7 technical support assistance and a free trial, so you can try it out before making a decision. Whether you need to recover, move, or back up your data, Webbyacad OST to PST Converter is a reliable option that gives you all the support you need to manage your Outlook data effectively.
Cracking AI Black Box - Strategies for Customer-centric Enterprise ExcellenceQuentin Reul
The democratization of Generative AI is ushering in a new era of innovation for enterprises. Discover how you can harness this powerful technology to deliver unparalleled customer value and securing a formidable competitive advantage in today's competitive market. In this session, you will learn how to:
- Identify high-impact customer needs with precision
- Harness the power of large language models to address specific customer needs effectively
- Implement AI responsibly to build trust and foster strong customer relationships
Whether you're at the early stages of your AI journey or looking to optimize existing initiatives, this session will provide you with actionable insights and strategies needed to leverage AI as a powerful catalyst for customer-driven enterprise success.
kk vathada _digital transformation frameworks_2024.pdfKIRAN KV
I'm excited to share my latest presentation on digital transformation frameworks from industry leaders like PwC, Cognizant, Gartner, McKinsey, Capgemini, MIT, and DXO. These frameworks are crucial for driving innovation and success in today's digital age. Whether you're a consultant, director, or head of digital transformation, these insights are tailored to help you lead your organization to new heights.
🔍 Featured Frameworks:
PwC's Framework: Grounded in Industry 4.0 with a focus on data and analytics, and digitizing product and service offerings.
Cognizant's Framework: Enhancing customer experience, incorporating new pricing models, and leveraging customer insights.
Gartner's Framework: Emphasizing shared understanding, leadership, and support teams for digital excellence.
McKinsey's 4D Framework: Discover, Design, Deliver, and De-risk to navigate digital change effectively.
Capgemini's Framework: Focus on customer experience, operational excellence, and business model innovation.
MIT’s Framework: Customer experience, operational processes, business models, digital capabilities, and leadership culture.
DXO's Framework: Business model innovation, digital customer experience, and digital organization & process transformation.
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesSAI KAILASH R
Explore the advantages and disadvantages of blockchain technology in this comprehensive SlideShare presentation. Blockchain, the backbone of cryptocurrencies like Bitcoin, is revolutionizing various industries by offering enhanced security, transparency, and efficiency. However, it also comes with challenges such as scalability issues and energy consumption. This presentation provides an in-depth analysis of the key benefits and drawbacks of blockchain, helping you understand its potential impact on the future of technology and business.
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
Smart mobility refers to the integration of advanced technologies and innovative solutions to create efficient, sustainable, and interconnected transportation systems. It encompasses various aspects of transportation, including public transit, shared mobility services, intelligent transportation systems, electric vehicles, and connected infrastructure. Smart mobility aims to improve the overall mobility experience by leveraging data, connectivity, and automation to enhance safety, reduce congestion, optimize transportation networks, and minimize environmental impacts.
Improving Learning Content Efficiency with Reusable Learning ContentEnterprise Knowledge
Enterprise Knowledge’s Emily Crockett, Content Engineering Consultant, presented “Improve Learning Content Efficiency with Reusable Learning Content” at the Learning Ideas conference on June 13th, 2024.
This presentation explored the basics of reusable learning content, including the types of reuse and the key benefits of reuse such as improved content maintenance efficiency, reduced organizational risk, and scalable differentiated instruction & personalization. After this primer on reuse, Crockett laid out the basic steps to start building reusable learning content alongside a real-life example and the technology stack needed to support dynamic content. Key objectives included:
- Be able to explain the difference between reusable learning content and duplicate content
- Explore how a well-designed learning content model can reduce duplicate content and improve your team’s efficiency
- Identify key tasks and steps in creating a learning content model
"Hands-on development experience using wasm Blazor", Furdak Vladyslav.pptxFwdays
I will share my personal experience of full-time development on wasm Blazor
What difficulties our team faced: life hacks with Blazor app routing, whether it is necessary to write JavaScript, which technology stack and architectural patterns we chose
What conclusions we made and what mistakes we committed
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
2. What is
App Engine?
An integrated software
architecture and full managed by
google~
Cloud
Datastore
Cloud
SQL
Cloud
Storage
Runtime
Cloud Endpoints Task Queues
8. Run use CLI locally
Using “gcloud preview app”
# cd $your-project
# gcloud preview app run ./app.yaml
9. Datastore
Unlimited store with transaction
support and high replicate
class Employee(db.Model):
name = db.StringProperty(required=True)
hire_date = db.DateProperty()
e = Employee(name="Antonio Salieri")
e.hire_date = datetime.datetime.now().date()
e_key = e.put()
scalable and
reliable storage
query
transactions
Bigtable
Megastore
Datastore
10. Memcache from google.appengine.api import memcache
...
value = memcache.get(key)
if value is None:
value = get_value_from_db(key)
if not memcache.add(key, value):
logging.error('Memcache add failed.')
...
Improve Application Performance
Reduce Application Cost