Like all good superheroes, every company has its own origin story explaining why they were created and how they grew over time. This article covers the origin story of QuestDB and frames it with an introduction to time series databases to show where we sit in that landscape today.
InfluxDB line protocol is a simple and convenient way to add data points to QuestDB. Now with authentication, your endpoint is more secure.
Every cab I have ever ridden has been complaining about how hard it is to make ends meet as a driver. The public is generally quick to blame unfair competition from the likes of Uber. However, additional forces are also to blame.
Going through more than 10 years worth of NYC taxi data, I analyse how the antiquated meter system impacts the livelihood of NYC cabbies by drawing an analogy with stock options trading. Interestingly, this approach allows us to show that drivers have progressively been worse-off, independently of competition from Uber.
In order to do so, we have loaded a dataset into our database QuestDB. This dataset includes over 1.6 billion taxi rides, 700 million FHV rides (Uber, Lyft etc), and 10 years of weather and gas prices data.
Thoughts on why speed and performance are crucial to time-series databases ingestion and analysis, originally posted in The New Stack.
A few weeks ago, I posted the story of how I started QuestDB on Hacker News. As it seems several people found the story interesting, I thought I would post it here.