This document provides an overview of configuration in Grails, including basic configuration, environments, data sources, dependency resolution, and more. The basic configuration files are BuildConfig.groovy and Config.groovy. BuildConfig.groovy contains settings for Grails commands while Config.groovy contains runtime settings. Both files can access implicit configuration variables. Environments like development, test, and production can be configured separately. Data sources are configured in DataSource.groovy and drivers are typically resolved using Ivy or Maven. Dependency resolution in Grails uses a DSL to control how plugins and JARs are resolved.
1. The runbook grants a user VPN access by making changes to their Active Directory profile after their request is approved. 2. It runs .NET scripts to extract the user's SAM account name and grant VPN access by setting the msnpallowdialin property to true. 3. It then gets information on the user and their manager from Active Directory to notify them by email that VPN access was granted.
MongoDB is the trusted document store we turn to when we have tough data store problems to solve. For this talk we are going to go a little bit off the path and explore what other roles we can fit MongoDB into. Others have discussed how to turn MongoDB’s capped collections into a publish/subscribe server. We stretch that a little further and turn MongoDB into a full fledged broker with both publish/subscribe and queue semantics, and a the ability to mix them. We will provide code and a running demo of the queue producers and consumers. Next we will turn to coordination services: We will explore the fundamental features and show how to implement them using MongoDB as the storage engine. Again we will show the code and demo the coordination of multiple applications.
The document discusses using Spring for Apache Hadoop to configure and run MapReduce jobs, Hive queries, Pig scripts, and interacting with HBase. It provides examples of configuring Hadoop, Hive, Pig, and HBase using Spring namespaces and templates. It demonstrates how to declare MapReduce jobs, run Hive queries and Pig scripts, and access HBase using the HBaseTemplate for a higher level of abstraction compared to the native HBase client.
The document discusses sample code for creating a Chat class with message, dateCreated, and lastUpdated properties in Groovy. It also defines a ChatController that uses scaffolding to automatically generate CRUD operations for the Chat class.
The document discusses the Japan Grails/Groovy User Group (JGGUG) meeting on November 19, 2009. It provides an agenda for the meeting including a presentation on the Grails Acegi Plugin by T. Yamamoto. It also summarizes how to use the Grails Cloud Tool plugin to deploy Grails applications to the cloud and Amazon Web Services.
The document discusses the Japan Grails/Groovy User Group (JGGUG). It notes that their next meeting will be on September 9-11 and will cover the Grails Acegi Plugin. It also provides links to the group's website and a twitter account for updates. Additionally, it shows examples of using Groovy and Grails for a web application that can be deployed to Google App Engine.
It's the presentation slides I prepared for my college workshop. This demonstrates how you can talk with PostgreSql db using python scripting.For queries, mail at dipeshsuwal@gmail.com
quick 5 minute tutorial. join multiple databases and create personalized email invoices with google apps script
Donner le goût du ReactiveCocoa. L’objectif de la présentation étant de nous expliquer comment migrer du code lié au KVO vers une implémentation ReactiveCocoa. Cette approche du KVO par ReactiveCocoa permet de comprendre très facilement les bases et l’intérêt de ce nouveau framework et de s’y mettre facilement et progressivement.
Example code using the Hadoop APIs directly from my April 2011 Atlanta Java Users Group presentation.
This document provides information on storing and processing big data with Apache Hadoop and Cassandra. It discusses how to install and configure Cassandra and Hadoop, perform basic operations with their command line interfaces, and implement simple MapReduce jobs in Hadoop. Key points include how to deploy Cassandra and Hadoop clusters, store and retrieve data from Cassandra using Hector and CQL, and use high-level interfaces like Hive and Pig with Hadoop.
Как известно, синтаксис запросов с агрегатными функциями и группировками в CoreData сложен для понимания и излишне раздут. Из доклада вы узнаете о небольшой библиотеке разработки самого Азиза, созданной для работы с CoreData.