New ‘Cloud Native Computing Foundation’ Trying to Standardize on Cloud and Containers

horovits

Cloud Native Computing Foundation (CNCF) is a new open standardization initiative recently formed under the Linux Foundation with the mission of providing standard reference architecture for cloud native applications and services, based on open-source software (OSS). The first OSS is Google’s Kubernetes, which was released in v1.0 the same day, and was donated by Google to the foundation.

Google is one of the 22 founding members, together with big names such as IBM, Intel, Redhat, VMware, AT&T, Cisco and Twitter, as well as important names in the containers realm such as Docker, Mesosphere, CoreOS and Joynet.

The announcement of the new foundation came only a few weeks after the announcement of the Open Container Initiative (OCI), also formed under the Linux Foundation. Even more interesting to note that almost half of the founding companies of CNCF are among the founders of OCI. According to the founders, these two initiatives are complementary: while OCI…

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Building microservices with Spring Boot – part 1

plain old objects

This article introduces the concept of a microservice architecture and the motivations for using this architectural approach. It then shows how Spring Boot, a relatively new project in the Spring ecosystem can be used to significantly simplify the development and deployment of a microservice. You can find the example code on github.

What are microservices?

Since the earliest days of Enterprise Java, the most common way of deploying an application has been to package all the application’s server-side components as a single war or ear file. This so-called monolithic architecture has a number of benefits. Monolithic applications are simple to develop since IDEs and other tools are oriented around developing a single application. They are also simple to deploy since you just have to deploy the one war/ear file on the appropriate container.

However, the monolithic approach becomes unwieldy for complex applications. A large monolithic application can be difficult…

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[spmarket 2]Announcing Spotify Infrastructure’s Googley Future[/spmarket][spmarket 32]Announcing Spotify Infrastructure’s Googley Future[/spmarket]

News

[spmarket 2]

 Editor’s Note: This blog post was written by Nicholas Harteau/VP, Engineering & Infrastructure

——–

 As a company most often associated with amazing music recommendations and awesome parties (not to mention life-changing employee benefits), it’s rare that we get to talk about the exciting world of technical infrastructure – the real power behind the music – but today is special. Today we are announcing that we’re working with the Google Cloud Platform team to provide platform infrastructure for Spotify, everywhere.

This is a big deal. At Spotify we are obsessed with providing a streaming experience that feels as though you have all the music in the world on your phone. Historically, we’ve taken a traditional approach to doing this: buying or leasing data-center space, server hardware and networking gear as close to our customers as possible. This approach has allowed us to give you music instantly, wherever…

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AWS Java documentation is fucked

A Really Good Rant

The problem

We recently had a use case that was a perfect for the Amazon Simple Workflow Service (SWF).

In the end it was quite simple to implement. I simply read the C# SWF Hello World example and implemented that in Java.

The actual Java example is a nightmare. The following is not a rant about Java, Design Patterns or Aspect Oriented programming. I actually don’t mind any of those. It’s a rant about a Hello World example that requires hours to even setup and comprehend and in the end abstracts the whole point of the exercise away.

The fucking ridiculous HelloWorld example

Let me show you why i do not like to use the Java examples and why in general the AWS documentation is complete shit.

Let me introduce you to the SWF Hello World Java example. It has a few prerequisites.

Step 1

Setting up the Framework with the Toolkit for Eclipse

I’m in…

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Python libraries for building recommender systems

FAROBA

Recommender systems or Collaborative filtering is the process of filtering for information using techniques involving collaboration among multiple agents. Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data, such as financial service institutions that integrate many financial sources; or in electronic commerce and web applications where the focus is on user data

Here is the list of python libraries for building recommender systems.

1.Crab

Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recom- mendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib). The engine aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. The…

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