Marblegate Capital Secures $137 Million to Enhance NYC Taxi Operations#USA#New_York_City#Marblegate_Capital#NYC_Taxi#Signal_Taxi
Marblegate Capital Secures $137 Million to Enhance NYC Taxi Operations
Marblegate Capital Corporation has successfully closed financings worth $137 million to bolster operations and growth in the NYC taxi industry.
third-news.com
January 6, 2026 at 12:01 AM
Marblegate Capital Secures $137 Million to Enhance NYC Taxi Operations#USA#New_York_City#Marblegate_Capital#NYC_Taxi#Signal_Taxi
Marblegate Capital Corporation Welcomes Michael Hutchby as CFO to Lead Financial Strategy#United_States#New_York#Marblegate_Capital#NYC_Taxi#Michael_Hutchby
Marblegate Capital Corporation Welcomes Michael Hutchby as CFO to Lead Financial Strategy
Marblegate Capital Corporation announces Michael Hutchby's appointment as CFO, bringing over two decades of experience to enhance financial operations and strategy.
third-news.com
July 1, 2025 at 12:17 PM
Marblegate Capital Corporation Welcomes Michael Hutchby as CFO to Lead Financial Strategy#United_States#New_York#Marblegate_Capital#NYC_Taxi#Michael_Hutchby
Marblegate Capital Merges to Transform the NYC Taxi Medallion Market#USA#New_York#Marblegate_Capital#NYC_Taxi#DePalma_Companies
Marblegate Capital Merges to Transform the NYC Taxi Medallion Market
Marblegate Capital Corporation has successfully completed its merger with Marblegate Acquisition Corp. and DePalma Companies to establish a leading NYC taxi medallion lender and fleet operator.
third-news.com
April 10, 2025 at 11:12 AM
Marblegate Capital Merges to Transform the NYC Taxi Medallion Market#USA#New_York#Marblegate_Capital#NYC_Taxi#DePalma_Companies
Ok, just looked at the benchmark overview and I’m a little disappointed. Comparing performance on a dataset of ~30 GBs tells me very little given it could all fit into RAM on commodity hardware. Like, the difference is up to chunking.
January 2, 2025 at 3:29 AM
Ok, just looked at the benchmark overview and I’m a little disappointed. Comparing performance on a dataset of ~30 GBs tells me very little given it could all fit into RAM on commodity hardware. Like, the difference is up to chunking.
open_dataset("nyc-taxi/")
nyc_taxi |>
filter(payment_type == "Credit card") |>
group_by(year, month) |>
write_dataset("nyc-taxi-credit")
Input is 1.7 billion rows (70GB), output is 500 million (15GB). Takes 3-4 mins on my laptop 🙂
#rstats (2/3)
nyc_taxi |>
filter(payment_type == "Credit card") |>
group_by(year, month) |>
write_dataset("nyc-taxi-credit")
Input is 1.7 billion rows (70GB), output is 500 million (15GB). Takes 3-4 mins on my laptop 🙂
#rstats (2/3)
November 23, 2024 at 12:55 AM
open_dataset("nyc-taxi/")
nyc_taxi |>
filter(payment_type == "Credit card") |>
group_by(year, month) |>
write_dataset("nyc-taxi-credit")
Input is 1.7 billion rows (70GB), output is 500 million (15GB). Takes 3-4 mins on my laptop 🙂
#rstats (2/3)
nyc_taxi |>
filter(payment_type == "Credit card") |>
group_by(year, month) |>
write_dataset("nyc-taxi-credit")
Input is 1.7 billion rows (70GB), output is 500 million (15GB). Takes 3-4 mins on my laptop 🙂
#rstats (2/3)
RT @djnavarro@fosstodon.org
My favourite trick for working with huge data sets in R. If your dataset is larger than memory and the query result is also larger than memory, you can still use dplyr/arrow pipelines. Example:
library(arrow)
library(dplyr)
nyc_taxi <- (1/3)
My favourite trick for working with huge data sets in R. If your dataset is larger than memory and the query result is also larger than memory, you can still use dplyr/arrow pipelines. Example:
library(arrow)
library(dplyr)
nyc_taxi <- (1/3)
November 23, 2024 at 12:49 AM
RT @djnavarro@fosstodon.org
My favourite trick for working with huge data sets in R. If your dataset is larger than memory and the query result is also larger than memory, you can still use dplyr/arrow pipelines. Example:
library(arrow)
library(dplyr)
nyc_taxi <- (1/3)
My favourite trick for working with huge data sets in R. If your dataset is larger than memory and the query result is also larger than memory, you can still use dplyr/arrow pipelines. Example:
library(arrow)
library(dplyr)
nyc_taxi <- (1/3)