My recent posts

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Design Patterns for Microservices

A nice article, a concise list of “Design Patterns for Microservices”, very useful as a reference and as a starting point for in-depth studies on the subject! Well done   Design Patterns for Microservices A detailed description of many design patterns for Microservices. Source: DZone or Madhuka Udantha blog

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Event Architecture

Useful event-sourcing Pattern!

A very interesting article that I find very useful! Here are shared some of the common producer models, which show how to transform a classic architecture to an Event Sourcing model. Below is the link to the complete article! In part one, we learned about how at Nordstrom we’ve been exploring and implementing event-sourcing as an architectural pattern. In part…...

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Something is changing

Have you noticed that something on the site does not work and more is new instead? That’s true! I’m trying to do something different and I’m in a creative phase. Stay tuned

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Success or Burnout? Q&A on How Personal Agility Can Help

How can you find out if you’re being successful or heading for a burnout? The only person who can really answer that question is you. A Q&A with Peter Stevens and Maria Matarelli who spoke about success or burnout and personal agility at eXperience Agile 2018. Source: Success or Burnout? Q&A on How Personal Agility Can Help

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Automating Datacenter Operations at Dropbox

  Introduction As a company that manages our own infrastructure we need to be able to rapidly install new server capacity and ensure that the equipment entering our production environment is highly reliable. Source: Automating Datacenter Operations at Dropbox

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Machine Learning Pipeline for Real-Time Forecasting @Uber Marketplace

On QCon.ai  Chong Sun and Danny Yuan discuss how Uber is using ML to improve their forecasting models, the architecture of their ML platform, and lessons learned running it in production. The meeting is well structured and divided into 3 large parts: The pipeline and why do forecasting, what is the challenge they face The Model Management ( 17:52m )...

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The Bash Hackers Wiki

Yes… the bash documentation that I’ve always looked for and that I’ve never found, I love this team! Declaring the intentions as: The main motivation was to provide human-readable documentation and information so users aren’t forced to read every bit of the Bash manpage – which can be difficult to understand. Source: The Bash Hackers Wiki [Bash Hackers Wiki] I...

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Evolutions of Big Data Analisys for streaming data and machine learning solutions

The ecosystem of Big Data analysis has evolved in recent years with new databases, streaming data and machine learning solutions which  require more than the classic deployment model. The revolution of Container technologies try to cover these new objectives and there are possible to accomplish in the organizations. Below 2 articles where you can start thinking about what is most...

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How Booking.com Uses Kubernetes for Machine Learning

Recommended reading: Sahil Dua explained how Booking.com has been able to scale machine learning (ML) models for recommending destinations and accommodation to their customers using Kubernetes, at QCon London conference. In particular, he stressed how Kubernetes elasticity and resource starvation avoidance on containers helps them run computationally (and data) intensive, hard to parallelize, machine learning models. Source: How Booking.com Uses...