Building Data Systems: What Do You Need?
In this post, we’re going to go over the capabilities you need to have in place in order to successfully build and maintain data systems and data infrastructure.
Posts Tagged ‘Technical’
Noteworthy Links: Hadoop Edition
Hadoop is 10 years old! Check out these related links.
Talking About the Caltrain
On May 6th, SVDS hosted an Open Data Science Conference (ODSC) Meetup in our Mountain View headquarters. Data Engineer Harrison Mebane and Data Scientist Christian Perez presented on our Caltrain project.
Working Effectively in Data Science Teams
On April 21st, SVDS hosted the WWCode Silicon Valley chapter in our Mountain View office; we gave a talk titled Working Effectively in Data Science Teams.
Jupyter Notebook for Data Science Teams
Data Scientist Jonathan Whitmore has just released a screencast tutorial for Jupyter Notebooks.
Building a Prediction Engine using Spark, Kudu, and Impala
In this post, Richard walks you through a demo based on the Meetup.com streaming API to illustrate how to predict demand in order to adjust resource allocation.
Noteworthy Links: Strata Edition
Here are some links from around the internet to get you in a Strata state of mind.
Why Notebooks Are Super-Charging Data Science
There is little limit to what can be done with a notebook. As well as the data science work you might expect, such as manipulating and graphing data, we’ve used them for sharing work on analytical tasks such as motion detection in video. In this post Edd takes a look at why we’re seeing notebooks everywhere.
Analyzing Caltrain Delays: What We Can Learn
In this post, we will explore some aspects of the train delay data we’ve been collecting from the Caltrain API over the past few months. The goal is to get our heads into the data before setting off on building a prediction model.