Many ask themselves, “Why would I use a semantic layer? What is it anyway?” In this hands-on guide, we’ll build the simplest possible semantic layer using just a YAML file and a Python script—not as the goal itself, but as a way to understand the value of semantic layers. We’ll then query 20 million NYC taxi records with consistent business metrics executed using DuckDB and Ibis. By the end, you’ll know exactly when a semantic layer solves real problems and when it’s overkill.| Data Engineering Blog
pandas is a library that provides functions to support data analysis in the Python programming language. NEC Research Laboratories has developed a library called FireDucks, a faster version of pandas. Data Preparation The analysis is performed on the data of passenger history of cabs in New York City. The source of the data is as follows: https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page To analyze large data sets, we downloaded and merged the “Yellow Taxi Trip Records” data fr...| fireducks-dev.github.io
The zero-copy integration between DuckDB and Apache Arrow allows for rapid analysis of larger than memory datasets in Python and R using either SQL or relational APIs.| DuckDB
In this post of the PyTorch Introduction, we’ll learn how to use custom datasets with PyTorch, particularly tabular, vision and text data| DareData Blog
The New York City Taxi & Limousine Commission has released a staggeringly detailed historical dataset covering over 1.1 billion individual taxi trips in the city from January 2009 through June 2015. …| toddwschneider.com
We recently pushed out two new and experimental features Coiled Jobs| phofl.github.io
Get the most out of PyArrow support in pandas and Dask right now| phofl.github.io
Over 50% of peak hour taxi trips would be faster as Citi Bike rides, and taxis are only getting slower| toddwschneider.com