In this session, we will go step by step through the creation of a Google App Engine (GAE) application using the Java Runtime. This will be a beginner level session with respect to Google App Engine, but, a good understanding of how to build web applications in Java will be assumed. The code of the twitter bot sample application is available online.
The document discusses Twitter's data APIs, including the Streaming API which provides a 1% sample of all tweets or tracks keywords in real-time, and the REST API which accesses past tweet data. It recommends creating a Twitter app, using optimized calls to the REST API to avoid errors, and leveraging open source libraries rather than re-inventing the wheel.
POSTMAN | Postman allows you to design, develop, test and monitor APIs. This talk will provide an overview of the interfaces, runtime and variables.
The document discusses the key components that make up the Meteor stack, including Blaze, Tracker, DDP, and Mongo. It notes that while Meteor uses these components by default, the framework is customizable - developers can replace the UI library, database, and DDP client/server with alternatives. The standard Meteor application architecture is then outlined, explaining how each component fits together and interacts to provide reactivity on the client.
Saltstack is by it's design a event driven configuration management tool. In talk will do a deep dive into salt reactor, runners and beacon systems. Talk will also cover a demo of event driven application releases process.
Spinnaker is an open source tool for deploying software releases to multiple cloud providers. Winnaker is a tool built by Target that helps automate common tasks when using Spinnaker like starting pipelines, getting stage details, integrating with chat tools, and troubleshooting errors. It removes company-specific code so others can contribute. Winnaker is distributed as a Docker container and makes it easy to pressure test Spinnaker and cloud environments by running multiple pipelines.
SpringOne Platform 2017 Prasad Bopardikar, Pivotal; Colin Stevenson, Pivotal "We all know Cloud Foundry is a great platform for cloud-native applications. However, what happens when you’re building an app that leverages services from public cloud providers such as Microsoft, Google and Amazon? Service brokers make it easy to spin up service instances and bind to apps. What about the actual code itself? Developers leverage the popular Spring Boot framework to quickly build Java apps to deploy to Cloud Foundry. The Spring Boot Starters and Auto-Configuration eliminate the need to write boilerplate code to consume some services, but not all. We’ve decided to give you a head start. This session is about extending the Spring framework. We’ll use examples from our recent work with Microsoft Azure, Google Cloud Platform and Amazon AWS services. As more services become available, developers will want to consume these on Cloud Foundry. Extend Spring to make it easier for developers to consume those backing services!"
This document outlines Daxtra's resume search integration solution. It involves using a candidate feed agent that loads resumes into an AWS SQS queue from various data sources. A resume reader then processes messages from the queue by looking up resumes, adding candidate profiles to Daxtra, and logging the results. A candidate search agent allows for searching candidates and returning results via API calls to Daxtra services.
This document provides links to resources for developing and deploying Spring Boot applications on Azure. It includes links to tutorials on deploying a first Spring Boot app, a workshop on Azure Spring Cloud, demo videos of Azure Spring Cloud, information on using Spring with Azure services, tutorials for building Java apps on Azure, best practices for deploying Spring Boot apps, and a sample app using Spring Security and Azure Active Directory.
SpringOne 2021 Session Title: Rapid Development with Azure Spring Cloud Speakers: Josh Long, Spring Developer Advocate at VMware; Julien Dubois, Java Developer Advocacy Manager at Microsoft; Layla Porter, Developer Advocate, .NET communities at VMware
So here we have our brand new app, but how about its deployment, autoscale, high availability and so on? We're gonna take a journey from source code posted to GitHub to deployment in Google Cloud Platform which will be capable to take bazillion of requests. Will make our own docker image and see how it is looks like in practice. Will start our first kubernetes cluster and deploy our app. Will have a sneak peak into autoscaler and high availability
Google App Engine (GAE) is a platform that allows developers to build and host web applications in Google's infrastructure. Key points: - GAE provides scalable hosting for web applications built using Python, Java, and other technologies. - It offers APIs for common services like data storage (BigTable), user authentication, mail sending, and image processing. - Applications can scale automatically based on traffic and are subject to resource limits of 500MB of storage and 500 concurrent requests.
The document describes two types of high storage data warehouse nodes - Extra Large (XL) and Eight Extra Large (8XL). The XL node has 2 virtual cores, 15GB memory, 3 HDDs with 2TB storage, and moderate network and disk I/O. The larger 8XL node has 16 virtual cores, 120GB memory, 24 HDDs with 16TB storage, 10GbE networking and very high disk I/O. Both nodes are designed for high storage workloads in a data warehouse environment.