Data Council Blog

Here's our November 2020 roundup of good reads and podcast episodes that might be relevant for your career in data:

1. Heroes of NLP: Quoc Le (Deeplearning.ai)

Here's our October 2020 roundup of good reads and podcast episodes that might be relevant to you as a data professional:

1. Multiplayer Editing: a Pragmatic Approach (Hex)

Here's our September 2020 roundup of good reads and podcast episodes that might be relevant to you as a data professional:

1. What Data Tools Don't Do (Data Council)

Our founder Pete Soderling co-authored a follow-on piece to his previous post with Great Expectations' core contributor Abe Gong and Partner at Amplify Partners Sarah Catanzaro, for which they had interviewed the makers of some of the hottest data tools. The focus is still the same: rather than what their data tools can do, we hear about what they don't do, as a way to better understand how they fit together. From ApertureData to Xplenty, this new installment covers 21 new tools, and you can read it here.

Here's our August 2020 roundup of good reads and great podcast episodes for anyone working with data:

1. Processing Large Datasets with Python

AI engineer and author J.T. Wolohan was recently a guest of the Heroku’s Code[ish] podcast to discuss the contents of his book, “Mastering Large Datasets with Python.” Listen to the episode here or read the transcript for some practical advice on using Python to deal with massive datasets, especially in the context of machine learning.

Here's our July 2020 roundup of relevant links for data professionals, from blog posts to podcast episodes:

1. The State of Airflow

Software Engineering Daily recently invited Apache Airflow's creator Maxime Beauchemin and Astronomer engineers Vikram Koka and Ash Berlin-Taylor to discuss the state of Airflow. Listen to the podcast episode or read the transcript to hear their comments on Airflow's use cases, its purpose, the open source ecosystem, and more.

Here's our June 2020 roundup of relevant links for data professionals, from blog posts to podcast episodes:

1. Self-Supervised Learning vs. AGI

"AGI does not exist — there is no such thing as general intelligence. We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence," Yann LeCun declared during an online session of the International Conference on Learning Representation (ICLR) 2020, which VentureBeat wrote about. Together with fellow Turing Award winner Yoshua Bengio, he advocated for pursuing humanlike AI through "self-supervised learning."

Analytics Engineer: this term has started showing up in blog posts and job listings. It all happened quickly; just a couple of years ago, it wasn't a thing our friends in the data ecosystem talked about. So how did it start trending, what is it exactly, and is it here to stay? We decided to take a closer look, and here's what we found out.

Cube.js is an open source analytics framework meant to answer the "lack of tools for software engineers who are building production, customer-facing applications and need to embed analytics features into these applications," its co-founder and CEO Artyom Keydunov explained in a blog post. 

Here's our monthly roundup of relevant links for data professionals, from blog posts and tutorials to podcast episodes:

1. Product Management for AI

Peter Skomoroch and Mike Loukides co-authored a very interesting post on what makes product management different in the context of AI. Based on the specificities of AI software development, they make a series of recommendations for a process that also takes business priorities into account. Their post also ends with a list of relevant resources, so it is worth checking out.