Existen ocasiones en las que requerimos que el contenido de una rama esté disponible en otra, sin realizar un merge, ya que esto implicaría arrastrar otras partes del historial. Esto se logra mediante el uso de cherrypick y, para simplificar la tarea, comenzamos realizando un squash. A continuación la receta:
Este libro fue escrito en 2006, con una segunda edición en 2017 - yo lo leí en 2022 y siento que lo leí 16 años más tarde de lo que debería!
El uso de ambientes virtuales permite isolar las dependencias del proyecto de otras instaladas en las carpetas de sistemas. Cada .venv contiene su propia version de los binarios de Python y sus dependencias. A continuación una simple demostración de como se podria organizar venv para compartir el proyecto de forma efectiva.
A couple of years ago I created a Spotify’s playlist where I add all tracks I liked, just as the main repository of things I’d like to listen to, no matter the mood I was when I added that song. As time goes, this playlist became less enjoyable to listen due to the change in rhythm - From listen to a Metal song it jumps to Bossa Nova, which is very annoying. This post contains a few data science approaches I applied to organize this playlist and what worked and what didn’t.
As I’m building a new startup I’ve been installing several Macbooks and helping engineers on their own setup, of course, we all have our kinks on configuring our machines but there is a base that would be nice to share and keep as standard as possible. Those are my (hopefully sane) defaults.
I’ve been working with static-typed languages for several years now, C# / Java / Scala developer and I like the safety and guarantees of having type checking, also the whole JVM ecosystem aged well with great building tools, libraries and lots of experience from the community. But I cannot ignore all the buzz around Javascript and I don’t want to be prejudiced about something I didn’t work directly for several years.
This is a very overdue blog post that I had on draft for almost an year, but I believe it is still very relevant yet.
Compartimos nuestra experiencia migrando de 100+ VMs a un cluster de Docker Swarm, los componentes y prácticas que brindaron estabilidad, control y cuales son nuestro planes futuros basado en ese aprendizaje.
If you never heard about Jupyter Notebook, I highly recommend you to check it out. It have been my primary platform to build reports and data driven case studies. On this post I’d like to show how I create a simple and isolated environment with a Bash script and Docker to run JupyterLab.
Recently Jupyter Notebook received a major overhauling and become JupyterLab - currently in beta, but the new platform looks fresh and very powerful.
Esa mañana tuve la oportunidad de conversar con Marcos Clavelino, conductor del programa “Vueltas en el Aire” de la Radio Universidad Nacional de La Plata donde conversamos sobre Big Data, Privacidad y el caso del supuesto leak de información de usuarios por parte de Facebook.
## Escuche completo acá
I’ve been working on analytics/big data field for 10+ years, during this time I’ve been working mostly with MySQL, MongoDB, Redis and Cassandra. Just a couple of years ago I started to really pay attention to Postgres, and my regret is not getting into it earlier… On this post I try to enumerate a few features I’m using and why I think you should try it too, before jumping into the architectural and operational complexity of multiple NoSQL.